From ff0817bae72074e68c132e5eb5a81f59567a7712 Mon Sep 17 00:00:00 2001
From: Jonathan <schimpf@hab.de>
Date: Thu, 16 Nov 2023 13:09:08 +0100
Subject: [PATCH] dimpel angelegt

---
 .../gattungssignal_2022_001.png               |  Bin 0 -> 59920 bytes
 .../gattungssignal_2022_002.png               |  Bin 0 -> 50079 bytes
 .../gattungssignal_2022_003.png               |  Bin 0 -> 43901 bytes
 .../gattungssignal_2022_004.png               |  Bin 0 -> 59295 bytes
 .../gattungssignal_2022_005.png               |  Bin 0 -> 58519 bytes
 .../gattungssignal_2022_006.png               |  Bin 0 -> 63357 bytes
 .../gattungssignal_2022_007.png               |  Bin 0 -> 62396 bytes
 .../gattungssignal_2022_008.png               |  Bin 0 -> 44438 bytes
 .../gattungssignal_2022_009.png               |  Bin 0 -> 65626 bytes
 .../gattungssignal_2022_010.png               |  Bin 0 -> 74214 bytes
 .../gattungssignal_2022_011.png               |  Bin 0 -> 59517 bytes
 .../gattungssignal_2022_v2_0.pdf              |  Bin 0 -> 261242 bytes
 .../gattungssignal_2022_v2_0.xml              | 3713 +++++++++++++++++
 13 files changed, 3713 insertions(+)
 create mode 100644 2022_009_dimpel_v2_0/gattungssignal_2022_001.png
 create mode 100644 2022_009_dimpel_v2_0/gattungssignal_2022_002.png
 create mode 100644 2022_009_dimpel_v2_0/gattungssignal_2022_003.png
 create mode 100644 2022_009_dimpel_v2_0/gattungssignal_2022_004.png
 create mode 100644 2022_009_dimpel_v2_0/gattungssignal_2022_005.png
 create mode 100644 2022_009_dimpel_v2_0/gattungssignal_2022_006.png
 create mode 100644 2022_009_dimpel_v2_0/gattungssignal_2022_007.png
 create mode 100644 2022_009_dimpel_v2_0/gattungssignal_2022_008.png
 create mode 100644 2022_009_dimpel_v2_0/gattungssignal_2022_009.png
 create mode 100644 2022_009_dimpel_v2_0/gattungssignal_2022_010.png
 create mode 100644 2022_009_dimpel_v2_0/gattungssignal_2022_011.png
 create mode 100644 2022_009_dimpel_v2_0/gattungssignal_2022_v2_0.pdf
 create mode 100644 2022_009_dimpel_v2_0/gattungssignal_2022_v2_0.xml

diff --git a/2022_009_dimpel_v2_0/gattungssignal_2022_001.png b/2022_009_dimpel_v2_0/gattungssignal_2022_001.png
new file mode 100644
index 0000000000000000000000000000000000000000..0b52316496b684b3a0ce9d6c72cf1f20e34f3515
GIT binary patch
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diff --git a/2022_009_dimpel_v2_0/gattungssignal_2022_006.png b/2022_009_dimpel_v2_0/gattungssignal_2022_006.png
new file mode 100644
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diff --git a/2022_009_dimpel_v2_0/gattungssignal_2022_v2_0.pdf b/2022_009_dimpel_v2_0/gattungssignal_2022_v2_0.pdf
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new file mode 100644
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--- /dev/null
+++ b/2022_009_dimpel_v2_0/gattungssignal_2022_v2_0.xml
@@ -0,0 +1,3713 @@
+<?xml version="1.0" encoding="utf-8"?>
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+   xmlns:tei="http://www.tei-c.org/ns/1.0" xmlns:xlink="http://www.w3.org/1999/xlink"
+   xmlns:xhtml="http://www.w3.org/1999/xhtml">
+   <teiHeader>
+      <fileDesc>
+         <titleStmt>
+            <title>
+               <biblStruct>
+                  <analytic>
+                     <title level="a">Gute Wörter, schwaches Gattungssignal. Differenzen zwischen
+                        Roman-Subgenres und Dramen mit Delta und signifikantem Wortschatz
+                        aufspüren</title>
+                     <respStmt>
+                           <persName>
+                                 <forename>Friedrich Michael</forename>
+                                 <surname>Dimpel</surname>
+                              <email>mail@dimpel.de</email>
+                              <idno type="gnd">1111656460</idno>
+                              <idno type="orcid">0000-0003-4833-4897</idno>
+                    <affiliation>Friedrich-Alexander-Universität Erlangen-Nürnberg</affiliation>
+                           </persName>
+                        <resp ref="http://id.loc.gov/vocabulary/relators/aut">Author</resp>
+                     </respStmt>
+                     <idno type="doi">10.17175/2022_009_v2</idno>
+                     <idno type="ppn">1866422553</idno>
+                     <idno type="zfdg">2022.009_v2</idno>
+                     <idno type="url">https://www.zfdg.de/node/470</idno>
+                     <date when="2022-11-17">17.11.2022</date>
+                     <date when="2023-11-14">14.11.2023</date>
+                  </analytic>
+                  <monogr>
+                     <title level="j">Zeitschrift für digitale Geisteswissenschaften</title>
+                     <respStmt>
+                        <resp>Publiziert von</resp>
+                        <orgName role="marc_pbl">Herzog August Bibliothek</orgName>
+                     </respStmt>
+                     <respStmt>
+                        <resp ref="http://id.loc.gov/vocabulary/relators/dtm">Transformation der Word Vorlage nach TEI</resp>
+                        <persName>
+                           <surname>Baumgarten</surname>
+                           <forename>Marcus</forename>
+                           <idno type="gnd">1192832655</idno>
+                           <idno type="orcid">0000-0003-0801-9462</idno>
+                        </persName>
+                     </respStmt>
+                     <availability status="free">
+                        <p>Available at <ref target="https://www.zfdg.de">https://www.zfdg.de</ref>
+                        </p>
+                     </availability>
+                     <imprint>
+                       <biblScope unit="year">2022</biblScope>
+                     <biblScope unit="artikel">09</biblScope>
+                     </imprint>
+                     </monogr>
+               </biblStruct>
+            </title>
+         </titleStmt>
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+            <edition>Elektronische Ausgabe nach TEI P5</edition>
+         </editionStmt>
+         <publicationStmt>
+            <distributor>
+               <name>
+                  <orgName>Herzog August Bibliothek Wolfenbüttel</orgName>
+               </name>
+            </distributor>
+            <idno type="doi">10.17175/zfdg.01</idno>
+            <idno type="ppn">0819494402</idno>
+            <authority>
+               <name>Herzog August Bibliothek</name>
+               <address>
+                  <addrLine>Lessingplatz 1</addrLine>
+                  <addrLine>38304 Wolfenbüttel</addrLine>
+               </address>
+            </authority>
+            <authority>
+               <name>Forschungsverbund Marbach Weimar Wolfenbüttel</name>
+               <address>
+                  <addrLine>Burgplatz 4</addrLine>
+                  <addrLine>99423 Weimar </addrLine>
+               </address>
+            </authority>
+            <availability status="free">
+               <p> Sofern nicht anders angegeben </p>
+               <licence target="http://creativecommons.org/licenses/by/4.0/">CC BY SA 4.0</licence>
+            </availability>
+            <availability status="free">
+               <p> Available at <ref target="workID">https://www.zfdg.de; (c) Forschungsverbund
+                     MWW</ref>
+               </p>
+            </availability>
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+         <editorialDecl>            
+            <p>Transformation der WORD-Vorlage nach XML/TEI-P5 durch TEI-Oxgarage und
+               XSLT-Skripten</p>
+            <p>Medienrechte liegen bei den Autor*innen</p>
+            <p xml:lang="de">Lektorat des Textes durch die Redaktion in Person von <persName>Caroline Jansky</persName>.</p>
+            <p>All links checked<date when="2023-10-30">30.10.2023</date>
+            </p>
+         </editorialDecl>
+      </encodingDesc>
+      <profileDesc>
+         <creation>Einreichung als Artikel der Zeitschrift für digitale
+            Geisteswissenschaften</creation>
+         <langUsage>
+            <language ident="de">Text auf Deutsch</language>
+            <language ident="de">Abstract auf Deutsch</language>
+            <language ident="en">Abstract auf Englisch</language>
+         </langUsage>
+         <textClass>
+            <keywords scheme="gnd">
+               <term>Gattungstheorie<ref target="4137287-6"/></term>
+                  <term>Literaturgattung<ref target="4074285-4"/></term>
+                     <term>Literaturwissenschaft<ref target="gnd/4036034-9"/></term>
+                        <term>Statistische Stilistik<ref target="1130711129"/>
+               </term>
+            </keywords>
+         </textClass>
+      </profileDesc>
+      <revisionDesc>         
+         <change when="2023-11-14" n="2.0" status="published">
+                        <p>Korrekturen entlang der Monita in den Gutachten: Sprachliche Verbesserungen; Ergänzungen in Kapitel 1 und 2 sowie in der Bibliografie; Ergänzung der Tabellenbeschriftungen, Aufschlüsselung von Abkürzungen</p>
+         </change>
+         
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+   </teiHeader>
+   <text>
+      <body>
+         <div>
+            <div type="abstract">
+               <argument xml:lang="de">
+                  <p>Untersucht wird, inwieweit sich die automatische Erkennung von Genres bzw.
+                     Subgenres mit Burrows’ Delta durch signifikantes Vokabular (›Gute Wörter‹) und
+                     Z-Wert-Begrenzung verbessern lässt. Auf einem Teilkorpus werden zu den Genres
+                     Abenteuerroman, Bildungsroman, Gesellschaftsroman, Komödie und Tragödie die
+                     ›Guten Wörter‹ ermittelt; auf einem zweiten Teilkorpus wird evaluiert. Bei
+                     allen fünf Textsorten steigen die F1-Werte durch diese Optimierungsmaßnahmen,
+                     beispielsweise von 0,65 auf 0,77. Bei Abenteuerroman, Bildungsroman und Komödie
+                     steigen die F1-Werte beispielsweise von 0,79 auf 0,91. Die Klassifikation von
+                     Abenteuerroman versus Drama und von Komödie versus Abenteuer- und Bildungsroman
+                     gelingt fehlerfrei (ARI=1). Während das Gute-Wörter-Verfahren den Recall
+                     steigert, begrenzt die Z-Wert-Begrenzung die False-Positives.</p>
+               </argument>
+               <argument xml:lang="en">
+                  <p>It is investigated to what extent the automatic recognition of genres or
+                     subgenres by means of Burrows’ Delta can be improved by significant vocabulary
+                     (›good words‹) and Z-value limitation. On one subcorpus, ›good words‹ are
+                     determined on the genres adventure novel, Bildungsromans, social novel, comedy,
+                     and tragedy; on a second subcorpus, they are evaluated. For all five text
+                     types, the F1 values increase due to these optimization measures, for example
+                     from 0.65 to 0.77. For adventure novel, Bildungsroman and comedy, the F1 values
+                     increase, for example, from 0.79 to 0.91. The classification of adventure novel
+                     versus drama and of comedy versus adventure and Bildungsroman succeeds without
+                     errors (ARI=1). While the ›good word procedure‹ increases recall, the Z-score
+                     limitation limits false positives.</p>
+               </argument>
+            </div>
+            <div type="chapter">
+               <head>1. Gattung und Stilometrie</head>
+            <p>Während die Autorschaftserkennung auf digitalem Weg gut erforscht ist und sehr gute
+               Erkennungsquoten vorweisen kann,<note type="footnote"> Vgl. etwa <ref type="bibliography" target="#Büttner_et_al_delta_2017">Büttner et&#160;al. 2017</ref>.</note> ist die digitale Erkennung von literarischen Gattungen deutlich
+               anspruchsvoller.<note type="footnote"> Vgl. <ref type="bibliography" target="#hettinger_et_al_classification_2016a">Hettinger et&#160;al. 2016a</ref>, S. 158.
+                  <ref type="bibliography" target="#hettinger_et_al_genre_2015">Hettinger et&#160;al. 2015</ref> berichten über eine verbesserte Erkennungsleistung mit Hilfe
+                  von SVMs, die auf der Basis von LDA-Topics erzielt wurde; vgl. ähnlich <ref type="bibliography" target="#hettinger_et_al_significance_2016b">Hettinger
+                     et&#160;al. 2016b</ref>. <ref type="bibliography" target="#kim_et_al_investigating_2017">Kim et&#160;al. 2017</ref> vergleichen englische Abenteuerromane, humoristische
+                  Romane, Science Fiction, Liebesgeschichten und Detektivromane auf der Grundlage
+                  von MFW-Bag-of-Words (Baseline), ›emotional arcs‹ und einem lexikalischen
+                  Emotionsmodell mit Hilfe von Maschinellem Lernen (RF und MLP). Vgl. weiterhin
+                  <ref type="bibliography" target="#schoech_computational_2020">Schöch 2020</ref>; <ref type="bibliography" target="#tello_gattungserkennung_2019">Calvo Tello 2019</ref>; <ref type="bibliography" target="#underwood_life-cycles_2016">Underwood 2016</ref>; <ref type="bibliography" target="#ardanuy_sporleder_clustering_2014">Ardanuy / Sporleder 2014</ref>; <ref type="bibliography" target="#underwood_et_al_mapping_2013">Underwood et&#160;al. 2013</ref>; <ref type="bibliography" target="#eder_rybicki_deeper_2011">Eder / Rybicki 2011</ref>; <ref type="bibliography" target="#sharoff_et_al_babel_2010">Sharoff et&#160;al. 2010</ref>; <ref type="bibliography" target="#stamatatos_et_al_categorization_2000">Stamatatos et&#160;al. 2000</ref>; <ref type="bibliography" target="#kessler_et_al_automatic_1997">Kessler et&#160;al.
+                     1997</ref>.</note> Während bei Texten der gleichen Autorin&#160;/&#160;des gleichen Autors trotz aller
+               Veränderungen im Laufe der Schaffensperiode und trotz
+               einer möglichen Intention auf wechselnde Stilformen doch von einem mit sich selbst
+               identischen Subjekt der Text-Origo ausgegangen werden kann, kann man Texte einer
+               Gattungen nur schwerlich einer gemeinsamen Origo-Instanz zuordnen. Zudem handelt es
+               sich bei der Zugehörigkeit eines Textes zu einer Gattung nicht um ein objektives
+               Faktum, sondern um ein Attributionsphänomen&#160;– Literaturwissenschaftler*innen
+               schreiben Texten die Eigenschaft zu, einer Gattung anzugehören. Solche Zuweisungen
+               sind nicht immer eindeutig möglich, da nicht wenige Texte im Spannungsfeld zwischen
+               mindestens zwei Gattungen verortet werden können;<note type="footnote"> Vgl. zu
+                  Gattungshybriden etwa <ref type="bibliography" target="#fuchs_hybride_1997">Fuchs 1997</ref>; <ref type="bibliography" target="#schulz_poetik_2000">Schulz 2000</ref>.</note> so zeichnet sich etwa der <bibl>
+                  <title type="desc">Willehalm</title>
+               </bibl> von Wolfram von Eschenbach durch Gattungsinterferenzen aus, in dem neben dem
+               Ãœberlebenskampfmotiv (ein Kennzeichen der <term type="dh">Heldenepik</term>) auch
+               zahlreiche höfische Passagen (Kennzeichen <term type="dh">höfischer Epik</term>)
+               vorkommen. Mitunter folgen Gattungszuweisungen auch pragmatischen Kriterien, wenn
+               etwa der <hi rend="italic">Willehalm</hi> für die Zwecke der Prüfungen im bayerischen
+               Staatsexamen rubriziert werden muss und in diesem Rahmen das vereindeutigende Etikett
+               ›Heldenepik‹ erhält. Dazu kommt, dass Gattungen nicht historisch konstant und
+               gleichförmig bleiben&#160;– Gabriel Viehhauser zeigt in seiner Studie zum
+               mittelalterlichen Minnesang, wie sich Gattungswandel auch digital abbilden
+               lässt.<note type="footnote"> Vgl. <ref type="bibliography" target="#viehhauser_gattungsgeschichten_2017">Viehhauser 2017</ref>.</note>
+            </p>
+            <p>In Studien zur Autorschaftsattribution wurden für schwierige Fälle unklarer
+               Autorschaft (etwa bei sehr kurzen und nicht-normalisierten mittelhochdeutschen
+               Texten) Optimierungsverfahren entwickelt&#160;– etwa das <term type="dh"
+                  >Gute-Wörter-Verfahren</term>, bei dem nicht alle <term type="dh"
+                  >Most-Frequent-Words</term> (MFW), sondern signifikante Wortformen verwendet
+                  werden.<note type="footnote"> Zur Verbesserung durch ›Gute Wörter‹ bei
+                     Autorschaftsfragen vgl. <ref type="bibliography" target="#dimpel_proisl_delta_2019">Dimpel / Proisl 2019</ref>.</note> In der vorliegenden Studie
+               soll geprüft werden, ob sich eine Verbesserung der Erkennungsleistung mit Hilfe des
+               Guten-Wörter-Verfahrens auch bei Gattungsfragen einstellt. Untersucht werden 100
+               deutsche Texte, die aus dem 19. Jahrhundert stammen oder die kurz davor bzw. danach
+               verfasst wurden. Damit das <term type="dh">Gattungssignal</term> möglichst
+               zielgerichtet und unbeeinflusst durch <term type="dh">Autorsignale</term> oder <term
+                  type="dh">Ãœbersetzersignale</term> untersucht werden kann, wird vermieden, mehrere
+               Texte der gleichen Autor*innen und ins Deutsche übersetzte Texte zu berücksichtigen.</p>
+               <p>Diese Studie ist also auf eine technische Fragestellung begrenzt&#160;– auf den Beitrag des Gute-Wörter-Verfahrens zu einer verbesserten automatischen Textsortenerkennung. Angestrebt wird nicht, traditionelle literaturwissenschaftliche Genre-Bestimmungen zu kritisieren oder zu präzisieren.<note type="footnote"><ref type="bibliography" target="#gittel_koeppe_distance_2022">Gittel&#160;/&#160;Köppe 2022</ref>, S.&#160;13–22, kritisieren die Studie von <ref type="bibliography" target="#underwood_life-cycles_2016">Underwood 2016</ref> für ihre Thesenbildung zu Genre-Grenzen, dem Generationen-Bezug von Genres und der Kohärenz von Genres, die auf der Grundlage von linguistischen Parametern mit Hilfe von maschinellem Lernen erfolgt. U.a. anhand der Textsorten Pastiche und Parodie stellen Gittel&#160;/&#160;Köppe in Frage, inwieweit linguistische Textmerkmale hinreichend für eine Genre-Bestimmung sein können.</note> Da versucht wird, eine Textsortenerkennung auf lexikalischer Basis vorzunehmen, könnte dieser Versuch als Modellierung<note type="footnote">Zum Modellieren als zentrale Tätigkeit im DH-Bereich vgl. <ref type="bibliography" target="#mccarty_humanities_2005">McCarty 2005</ref>.</note> einer Unterscheidbarkeit von Textsorten beschrieben werden. Für Computermodelle ist wie auch bei herkömmlichen Modellen das Merkmal der verkürzten Repräsentation wesentlich&#160;– das modellierte Objekt wird nicht vollständig durch das Modell abgebildet.<note type="footnote"><ref type="bibliography" target="#stachowiak_modelltheorie_1973">Stachowiak 1973</ref>, S.&#160;132. Vgl. zur Approximation bei der Modellbildung <ref type="bibliography" target="#saam-gautschi_modellbildung_2015">Saam&#160;/&#160;Gautschi 2015</ref>, S.&#160;26–38. <ref type="bibliography" target="#mccarty_humanities_2005">McCarty 2005</ref>, S 24, weist darauf hin, dass auch in der traditionellen Literaturwissenschaft Modelle omnipräsent sind&#160;– etwa bei der Beschreibung von Epochen. <ref type="bibliography" target="#gittel_koeppe_distance_2022">Gittel&#160;/&#160;Köppe 2022</ref>, S.&#160;20, kritisieren insbesondere, dass die digitale Modellierung von Underwood anders als literaturwissenschaftliche Beschreibungen Kontexte und paratextuelle Informationen nicht einbeziehen, diese können jedoch für die Erkennung der Gattung eines Textes entscheidend sein.</note> Aufgrund dieser Verkürzung ist es in der Regel problematisch, davon zu sprechen, dass sich Ergebnisdaten, die auf der Basis von digitalen Modellen gewonnen werden, unmittelbar dafür eignen, etwa hermeneutische Thesen zu verifizieren oder zu falsifizieren.<note type="footnote">Zur Trennung von Ergebnisdaten und Interpretation vgl. auch <ref type="bibliography" target="#dimpel_computerphilologe_2015">Dimpel 2015</ref>.</note></p>
+               <p>Gegenüber Studien, die auf Black-Box-Verfahren wie Maschinelles Lernen (siehe Anmerkung 2) setzen, haben Burrows’ Delta und das Gute-Wörter-Verfahren den Vorteil, dass die Berechnungsgrundlage transparent nachvollzogen werden kann. Zudem lässt sich überprüfen, welche Wortformen zur Textsortenunterscheidung besonders gut beitragen (siehe Anhang, <ref type="intern" target="#tab015">Tabelle 15</ref>).</p>
+            </div>
+            <div type="chapter">
+               <head>2. Korpusgestaltung und Präprocessing</head>
+            
+            <p>Verwendet wurden Texte, die im Internet frei verfügbar sind. Die meisten Texte
+               entstammen dem Textgrid-Repository. Die Texte wurden dann als Abenteuerroman,
+               Bildungsroman, Gesellschaftsroman, Komödie oder Tragödie eingestuft, wenn in einer
+               literaturgeschichtlichen Darstellung oder in einem Forschungsbeitrag eine
+               einschlägige Bezeichnung vorgefunden wurde.<note type="footnote">Dieses Verfahren lässt sich durchaus kritisieren: Bedacht wird dabei nicht, wie oft einem Text die Eigenschaft zugesprochen wird, zu einer Textsorte zu gehören. Unberücksichtigt bleibt auch, ob die Zuordnungen auf einheitlichen Genre-Definitionen basieren. Um eigene Textsortenmodelle zu entwickeln und die Zuordnungen auf dieser Basis zu überprüfen, standen für die Studie nicht die nötigen Ressourcen zur Verfügung. Insoweit deviante Epochenbegriffe eingehen sollten, würden damit allerdings gelebte Praktiken im Fach berücksichtigt.</note></p>
+            <p>Während der Einfluss des Übersetzersignals noch weniger gut erforscht ist,<note
+               type="footnote"> Vgl. <ref type="bibliography" target="#büttner_proisl_stilometrie_2016">Büttner / Proisl 2016</ref>.</note> kann das Autorsignal als ein
+               starkes stilometrisches Signal gelten.<note type="footnote"> Vgl. <ref type="bibliography" target="#schoech_corneille_2014">Schöch 2014</ref>.</note>
+               Falls beispielsweise bei der Untersuchung von Romansubgenres zahlreiche Texte von
+               Karl May im Korpus vorhanden sind, ist denkbar, dass die Erkennung des Subgenres
+               Abenteuerroman durch das Autorsignal von Karl May positiv beeinflusst wird&#160;– bei
+               nicht wenigen Autor*innen ist eine gewisse Präferenz für eine oder wenige Gattungen
+               erkennbar. Umgekehrt sind auch Fälle denkbar, in denen die gleichen Autor*innen in
+               verschiedenen Gattungen wirken, so dass ihre Texte aufgrund des Autorsignals
+               zusammenclustern, obwohl sie verschiedenen Gattungen zugeschrieben werden. Hettinger
+               et&#160;al. berichten von einem Sinken der Erkennungsleistung, wenn man Autorduplikate aus
+               dem zuvor examinierten Korpus herausnimmt.<note type="footnote"> <ref type="bibliography" target="#hettinger_et_al_classification_2016a">Hettinger et&#160;al.
+                  2016a</ref>, S. 161.</note>
+            </p>
+            <p>Ein Problem bei der Korpus-Zusammenstellung sind Gattungsinterferenzen und mehrfache
+               Labels: So ist Wilhelm Raabes <bibl>
+                  <title type="desc">Abu Telfan</title>
+               </bibl>
+               <hi rend="italic"> oder </hi>
+               <bibl>
+                  <title type="desc">Die Heimkehr vom Mondgebirge</title>
+               </bibl> sowohl als Entwicklungsroman, Gesellschaftsroman, Abenteuerroman,
+               Bildungsroman als auch Zeitroman eingestuft worden. Rolf Selbmann etwa bespricht
+               einige offenbar als prototypisch verstandene Bildungsromane unter der Ãœberschrift
+                  <quote>Zwischen Individualroman und Gesellschaftsroman</quote>
+               <note type="footnote"> Vgl. <ref type="bibliography" target="#selbmann_bildungsroman_1994">Selbmann 1994</ref>, S.&#160;96–120.</note>; andernorts beruft sich
+               Selbmann auf Benno von Wiese, der <quote>die <hi rend="italic">Epigonen</hi> [Immermann] zugleich als
+                  ›Entwicklungsroman‹, als ›Abenteuerroman‹, als ›modernen Roman‹, wie auch als
+                  ›gesellschaftlichen Zeitroman‹</quote> versteht.<note type="footnote"> <ref type="bibliography" target="#selbmann_bildungsroman_1994">Selbmann
+                     1994</ref>, S. 111.</note>
+            </p>
+            <p>Bei der Korpus-Erstellung wurden Texte vermieden, die beispielsweise sowohl als
+               Gesellschaftsroman als auch als Bildungsroman bezeichnet wurden, wodurch sich die
+               Zahl der verfügbaren Texte deutlich reduziert hat. Weitere Einschränkungen ergaben
+               sich durch das Vermeiden von Ãœbersetzungen und Autorenduplikaten. Nur mit einiger
+               Mühe war es möglich, je Textsorte 20 digitale Texte zu finden, die diese drei
+               Kriterien erfüllt haben. Weiterhin wurden stark dialektal geprägte Texte wie
+               ‚Sozialaristokraten‘ von Arno Holz nicht ins Korpus genommen. Selbstverständlich wäre
+               es wünschenswert, diese Tests auf einer breiteren Textgrundlage wiederholen zu
+               können.</p>
+            <p>Für die Evaluierung des Gute-Wörter-Verfahrens wurden zwei überschneidungsfreie
+               Teilkorpora verwendet: Die 50 Texte der Ermittlungsgruppe, auf deren Grundlage die
+               Gute-Wörter-Listen berechnet werden, sind nicht enthalten in der Kontrollgruppe
+               (ebenfalls 50 Texte), die die Qualität der Gattungserkennung erfasst. </p>
+            <p>Autorduplikate im Korpus haben sich zwar nicht ganz vermeiden lassen, aber es konnten
+               doch Vorkehrungen getroffen werden, dass Autorduplikate weder bei der Berechnung der
+               Guten Wörter noch bei der Evaluation im jeweiligen Test berücksichtigt wurden.
+               Doppelte Autor*innen, die jeweils einmal in der Kontrollgruppe und einmal in der
+               Ermittlungsgruppe vorhanden sind, sind unproblematisch. Sichergestellt ist zudem,
+               dass innerhalb einer Textsorte in den jeweils zehn Texten der Ermittlungs- und
+               Kontrollgruppe kein Autorenduplikat vorkommt. Zudem wurden in den Fällen, in denen
+               sich doppelte Autor*innen innerhalb der Kontroll- bzw. Ermittlungsgruppe nicht ganz
+               vermeiden lassen, Texte der Duplikat-Autor*innen nur als <term type="dh"
+                  >Ratetext</term> und nie als Vergleichstext im Vergleichskorpus (dazu mehr im <ref
+                  type="intern" target="#hd3">folgenden Abschnitt</ref>) verwendet, so dass in jedem
+               einzelnen Testlauf ausschließlich Texte verschiedener Autor*innen verwendet wurden. </p>
+            <p>Im Vorfeld der Tests wurden einige Präprocessing-Schritte unternommen. Bei den Dramen
+               habe ich die Regieanweisungen und die Sprecher*innenangaben entfernt. Die
+               Zeichensätze wurden nach <term type="dh">ANSI</term> vereinheitlicht, Sonderzeichen
+               mit Ausnahme der deutschen Umlaute wurden vereinheitlicht, Groß- in Kleinbuchstaben
+               konvertiert, Zahlen eliminiert. Weiterhin wurden die ersten 10&#160;% der <term type="dh"
+                  >Token</term> entfernt&#160;– mit diesem verbreiteten Verfahren werden paratextuelle Informationen und Besonderheiten am Textanfang beseitigt. </p>
+            </div>
+             <div type="chapter">
+               <head>3. Gute Wörter berechnen&#160;– Ermittlungsgruppe</head>
+            
+            <p>Das Verfahren zur Ermittlung der Guten Wörter ist ausführlich dokumentiert.<note
+               type="footnote"> <ref type="bibliography" target="#dimpel_töpfchen_2018a">Dimpel 2018a</ref>; <ref type="bibliography" target="#dimpel_et_al_streit_2019">Dimpel et&#160;al. 2019</ref>; vgl. weiterhin <ref type="bibliography" target="#dimpel_delta_2018b">Dimpel 2018b</ref>.
+                  Ein didaktisch aufbereiteter Foliensatz steht <ref target="https://doi.org/10.17879/55189462574">hier</ref>. </note> Für das
+               Setting ist elementar, dass ein Text als Ratetext verwendet wird und gegen ein
+               Vergleichskorpus mit meist 15 bis 30 <term type="dh">Distraktortexten</term> getestet
+               wird. Das Vergleichskorpus enthält jedoch auch einen Vergleichstext der Zielklasse&#160;–
+               bei Autorschaftsfragen ist also ein Text von der Autorin&#160;/&#160;dem Autor im Vergleichskorpus,
+               von der&#160;/&#160;dem auch der Ratetext stammt; bei Gattungsfragen ein Vergleichstext der
+               gleichen Gattung. </p>
+            <p>Wie bei Burrows’ Delta üblich, wird für jedes Wort der <term type="dh"
+                  >Most-Frequent-Words</term> (MFWs) die relative Häufigkeit gezählt,
+               Standardabweichung und <term type="dh">Z-Werte</term> berechnet und sodann die
+               Z-Wert-Differenz zwischen dem Ratetext und jedem Vergleichstext. Zentral für die
+               Ermittlung der Guten Wörter sind die <term type="dh">Level-2-Differenzen</term>, die
+               man berechnet als Differenz aus der Z-Wert-Differenz zwischen Ratetext und
+               Distraktortext einerseits und der Z-Wert-Differenz zwischen Ratetext und dem
+               Vergleichstext der Zielklasse andererseits. Auf positiven Level-2-Differenzen beruht
+               eine funktionierende Erkennung der Zielklasse. Negative Level-2-Differenzen sind ein
+               Störfaktor für die Erkennung der Zielklasse. </p>
+            <p>In einem Setting mit nur einem Distraktortext und zwei Texten der gleichen Klasse ist
+               mathematisch unmittelbar evident, dass Wörter mit positiver Level-2-Differenz zu
+               einem niedrigen <term type="dh">Delta-Wert</term> beitragen. In einem größeren
+               Setting mit mehreren Distraktortexten sind verschiedene Parameter denkbar, mit deren
+               Hilfe die Liste der Guten Wörter erstellt werden kann. Dimpel / Proisl haben gezeigt,
+               dass <term type="dh">Parametersets</term> mit einem <term type="dh"
+                  >Spitzenwertkriterium</term> zwar eine besonders gute Leistung bei
+               Autorschaftserkennung erbringen, jedoch auch so viele <term type="dh"
+                  >False-Positives</term> produzieren, dass dieses Parameterset problematisch
+               ist.<note type="footnote"> In <ref type="bibliography" target="#dimpel_proisl_delta_2019">Dimpel / Proisl 2019</ref>.</note>
+            </p>
+            <p>Verwendet wird für jede Textsorte nun eine Liste mit den Wortformen der
+               durchschnittlich höchsten Level-2-Differenzen von allen Ratetexten zu allen
+               Distraktortexten. Um diese Liste der Mittelwerte an hohen Level-2-Differenzen zu
+               erstellen, wird jeweils einer von zehn Texten der Zielgattung ins Distraktorkorpus
+               als Gattungsvergleichstext gegeben. Die neun anderen Texte der Ermittlungsgruppe der
+               jeweiligen Gattung werden reihum als Ratetext verwendet. Zu dem Ratetext, dem
+               Gattungsvergleichstext und je einem der Distraktortexte wird die Level-2-Differenz
+               berechnet. Aus diesen Level-2-Differenzen wird der Mittelwert der Level-2-Differenzen
+               für diesen Ratetext und diesen Gattungsvergleichstext zu allen 20 Distraktortexten
+               gebildet. Bei einem Gattungsvergleichstext und neun Ratetexten fallen für jede
+               Wortform neun durchschnittliche Level-2-Differenzen an. Dieses Verfahren wird zehnmal
+               wiederholt, so dass reihum jeder Text der Ermittlungsgruppe als
+               Gattungsvergleichstext ins Distraktorkorpus gegeben wird und die anderen neun Texte
+               als Ratetexte ›gegen‹ diesen getestet werden. Es fallen also insgesamt pro Wortform
+               20 × 9 × 10 Level-2-Differenzen an, aus denen schließlich ein weiterer Mittelwert
+               gebildet wird. Dieses Verfahren wird für jede Textsorte durchgeführt, es fallen also
+               fünf textsortenspezifische Listen mit Guten Wörtern an. </p>
+            <p>Im Distraktorkorpus befinden sich für jede der vier Textsorten der Nicht-Zielklasse
+               jeweils die Ermittlungsgruppentexte mit Nummern 01–05. Da für die wenigen
+               Autorduplikate im Ermittlungsgruppenkorpus hohe Nummern (08, 09) vergeben wurden, ist
+               bei Bildung der Gute-Wörter-Listen kein Autoduplikat im Spiel.<note type="footnote">
+                  Weitere Parameter für die Ermittlung der Guten Wörter: Verwendet wurden volle
+                  Texte nach Entfernung der ersten 10&#160;% der Wortformen. Die häufigsten 1.200 MFWs
+                  wurden verwendet. Experimente mit 1.500 MFWs haben schlechtere Ergebnisse
+                  hervorgebracht. Dies hängt vermutlich damit zusammen, dass die Komödien und
+                  Tragödien teils recht kurz sind. Der kürzeste Text kommt nach dem Entfernen der
+                  ersten 10&#160;% auf 5.473 Wortformen. Aus Rechenzeitgründen wurden Wortformen nach
+                  75.000 Wortformen nicht mehr berücksichtigt (Cutoff)&#160;– über die Hälfte der Texte
+                  ist ohnehin nicht länger als 50.000 Wortformen.</note>
+            </p>
+            <p>Zudem soll vermieden werden, dass Wortformen, die in den Ratetexten&#160;– also innerhalb
+               der Zielgattung&#160;– recht selten vorkommen, berücksichtigt werden. Damit eine Wortform
+               bei der Bildung der Liste der Guten Wörter berücksichtigt wird, muss sie in
+               mindestens vier von neun Ratetexten vorkommen. Damit sollen Eigenheiten von
+               Einzeltexten, die mutmaßlich weniger relevant für die Gattung sind,
+               unberücksichtigt bleiben. Dass es sich bei dem Parameter ›4 von 9‹ um einen
+               geeigneten Parameter handelt, wurde in Prätests mit kleinem Korpus und niedriger
+               Iterationszahl ermittelt.<note type="footnote"> In einem weiteren Prätest wurde
+                  zunächst versucht, jeweils fünf Texte der Ermittlungsgruppe in einen
+                  Pseudo-Gattungstext zusammen zu kopieren (mit Cutoff bei 75.000 Wortformen) und
+                  diese Datei als Vergleichstext der Zielklasse im Vergleichskorpus zu verwenden.
+                  ›Gegen‹ dieses Vergleichskorpus wurden einzeln die übrigen fünf Texte der
+                  Ermittlungsgruppe als Ratetexte getestet. Die Gute-Wörter-Listen, die in diesem
+                  Verfahren erzeugt wurden, haben ebenfalls schlechtere Ergebnisse hervorgebracht
+                  als die Listen, die im oben beschriebenen ›Reihum‹-Verfahren generiert
+                  wurden.</note>
+            </p>
+            </div>
+            <div>
+               <p></p>
+               <p></p>
+               <p></p>
+               <p></p>
+            </div>
+            <div type="chapter">
+               <head>4. Evaluierung&#160;– Kontrollgruppe </head>
+            
+            <p>Die fünf Listen der Guten Wörter werden in vier textsortenbezogenen Kombinationen mit
+               Texten der Kontrollgruppe evaluiert:</p>
+               <list type="unordered">
+            <item>Test A) Fünf Textsorten: ABE, BIL, GES, KOM, TRA<note type="footnote"> Abkürzungen:
+                  ABE: Abenteuerroman, BIL: Bildungsroman, GES: Gesellschaftsroman, KOM: Komödie,
+                  TRA: Tragödie.</note></item>
+<item>
+Test B) Drei Textsorten: ABE, BIL, KOM</item>
+                  <item>Test C) Drei Textsorten: ABE, KOM, TRA (ohne verschiedene Roman-Subgenres)</item>
+                  <item>Test D) Drei Textsorten: ABE, BIL, GES (ausschließlich Roman-Subgenres)</item>
+                  </list>
+            <p>Da die Unterscheidung von Bildungs- und Gesellschaftsroman aufgrund der thematischen
+               Nähe beider Subgenres eine besondere Herausforderung darstellt, ist für die
+               Testreihen B und C die beste Unterscheidungsleistung zu erwarten. </p>
+            <p>Für die Testreihen A und D wird angelehnt an Studien zu mittelhochdeutschen
+               Texten<note type="footnote"> Vgl. etwa <ref type="bibliography" target="#Büttner_et_al_delta_2017">Büttner et&#160;al. 2017</ref>.</note> zunächst ein
+               reiner <term type="dh">Erkennungsquotentest</term> mit fünf Vergleichstexten der
+               Zielklasse durchgeführt; für alle vier Testreihen wird ein ARI-Test (<term type="dh"
+                  >Adjusted Rand Index</term>) durchgeführt, bei dem zusätzlich auch die
+               Erkennungsquoten (<term type="dh">Recall</term>), False-Positives und <term type="dh"
+                  >F1-Werte</term> ausgegeben werden&#160;– zum Setting siehe unten.</p>
+            <p>Die kürzeste Liste der Guten Wörter, die alle Wortformen mit einer Level-2-Differenz
+               von &gt;0,2 enthält, umfasst bei den Komödien 495 Wortformen, die längste Liste bei
+               den Abenteuerromanen 637 Einträge. Eine Level-2-Differenz von &gt;0,4 ist bei den
+               Komödien bei den Wortformen mit den Nummern 1–254 vorhanden, bei den Abenteuerromanen
+               bei den Wortformen 1–189. Auf einen Test, der exakt die in Dimpel / Proisl 2019
+               geprüften Schwellenwerte ermittelt, wird verzichtet; getestet wird vielmehr mit 200,
+               300 und 400 MFWs. Wenn die Guten Wörter nicht in ausreichend vielen Texten im
+               aktuellen Test vorhanden sind,<note type="footnote"> Weiterhin werden von der
+                  MFW-Liste nur Wortformen verwendet, die in mindestens zwei verschiedenen Texten
+                  des Korpus vorkommen. Theoretisch denkbar ist, dass in einem Text beispielsweise
+                  ein Figurenname derart hochfrequent vorkommt, dass er in die Liste der 400
+                  häufigsten Wörter gelangt.</note> wird das Gute Wort nicht verwendet. Wenn dadurch
+               nicht mehr ausreichend viele Gute Wörter vorhanden sind, wird die MFW-Liste im
+               jeweiligen Test mit herkömmlichen MFWs ergänzt. Es werden also nicht unbedingt
+               ausschließlich Gute Wörter berücksichtigt; insofern ist im Folgenden auch von einer
+                  <hi rend="italic">bevorzugten Verwendung der Guten Wörter</hi> die Rede.</p>
+            <p>Näherungsweise bildet ein Test mit 200 MFWs einen Level-2-Differenzen-Mittelwert
+               &gt;0,4 und ein Test mit 200 MFWs einen Level-2-Differenzen-Mittelwert &gt;0,2 ab. In
+               der Liste für die Gesellschaftsromane&#160;– sie liegt hinsichtlich ihrer Länge im
+               Mittelfeld&#160;– ist bei Wortform Nr. 300 eine Level-2-Differenz von 0,32 vorhanden.</p>
+            <div type="subchapter">
+               <head>4.1 Setting: Erkennungsquotentest mit fünf Vergleichstexten der Zielklasse</head>
+              <p>Bei dieser Evaluierung kommen fast alle 50 Texte der Kontrollgruppe zum Einsatz. In
+               das Vergleichskorpus werden jeweils fünf (von zehn in der Kontrollgruppe vorhandenen)
+               Texte pro Gattung per Losverfahren gegeben, die nicht zu der Zielgattung, für die
+               jeweils auch die Guten Wörter berechnet wurden, gehören. Bei der Testreihe A) werden
+               also für vier Gattungen je fünf Distraktortexte ausgelost, insgesamt damit 20
+               Distraktortexte. </p>
+            <p>Wenn ein Text als Distraktortext gelost wird, zu dem ein Autorenduplikat in den
+               Kontrollgruppentexten der Zielgattung vorhanden ist, dann wird dieser Distraktortext
+               zurückgelegt; stattdessen wird ein anderer Distraktortext dieser Gattung verwendet.
+               Es ist also sichergestellt, dass die drei Textpaare, deren Autor*innen in den
+               Kontrollgruppentexten doppelt vertreten sind, nur als Ratetexte und nicht als
+               Distraktortexte im Vergleichskorpus berücksichtigt werden, so dass auch hier
+               Autorenduplikate das Gattungssignal nicht überlagern können.<note type="footnote"> In
+                  der Kontrollgruppe sind drei Duplikat-Paare vorhanden: Jean Paul (›bil_19,
+                  Flegeljahre‹, ›ges_13, Blumen, Frucht und Dornenstücke‹), Heinrich Laube (›ges_15,
+                  Junges Europa‹, ›tra_12, Monaldeschi‹) und Friedrich Schiller (›abe 12,
+                  Geisterseher‹, ›tra 16, Wallensteins Lager‹).</note>
+            </p>
+            <p>Bei der Zielgattung wird zunächst reihum jeweils einer der zehn Kontrollgruppentexten
+               als Vergleichstext ins Vergleichskorpus gegeben. Vier weitere Texte der Zielgattung
+               werden jeweils ebenfalls als Vergleichstexte dem Vergleichskorpus zugelost. Die
+               übrigen fünf Kontrollgruppentexte der Zielgattung werden als Ratetexte verwendet. </p>
+            <p>Wenn dieser Test mit Volltexten (gekürzt auf 100.000 Wortformen) durchgeführt wird,
+               werden pro Gattung zehn Durchgänge absolviert, um die Zufälligkeiten bei der
+               Auslosung auszugleichen; verwendet werden die Durchschnittswerte aller Durchgänge.
+               Wenn bei den Tests das <term type="dh">Bag-of-Words-Verfahren</term> zum Einsatz
+               kommt, wird die Textzusammenstellung für jeweils 200 Bag-of-Words pro Gattung neu
+               ausgelost. Als Bag-of-Words-Größe wird 10.000 Wortformen angesetzt. Standardmodus ist
+               ›Ziehen ohne Zurücklegen‹. Wenn ein Text&#160;– wie bei den kürzeren Komödien und
+               Tragödien&#160;– weniger als 11.000 Wortformen umfasst, gilt für diesen Text der Modus
+               ›Ziehen mit Zurücklegen‹.</p>
+            <p>Insgesamt befinden sich fünf Vergleichstexte der Zielgattung und 20 Distraktortexte
+               (bei Test A) bzw. zehn Distraktortexte (bei den Tests B–D) im Vergleichskorpus. Die
+               erwartete Erkennungsquote bei einer Zufallsverteilung liegt damit bei 20&#160;% (A) bzw.
+               bei 33&#160;% (B–D). </p>
+            <p>Da in der vorliegenden Studie überprüft werden soll, ob und inwieweit die bevorzugte
+               Berücksichtigung der Guten Wörter zu einer verbesserten Textsortenerkennung führt,
+               wird als Baseline im jeweiligen Test das gewählte Verfahren ohne Gute-Wörter-Liste
+               und ohne Z-Wert-Begrenzung betrachtet. Eine allgemeine Baseline kann nicht angegeben
+               werden: Einige der Studien, die in Fußnote 2 genannt sind, kommen zu F1-Werten etwas
+               über 0,8, manche kommen zu etwas höheren, andere auch teils zu deutlich niedrigeren
+               Ergebnissen. Allerdings sind die Studien nicht vergleichbar: Verwendet werden
+               verschiedene Korpora, verschiedene Sprachen, teils übersetzte Texte, verschiedene
+               Genres bzw. Subgenres, teils auch nicht-literarische Texte, verschiedene
+               Analyseverfahren und Auswertungsmethoden. Der Umgang mit Autorduplikaten ist ebenso
+               wenig einheitlich wie der Umgang mit mehrfachen Gattungslabels.<note type="footnote">
+                  <ref type="bibliography" target="#ardanuy_sporleder_clustering_2014">Ardanuy / Sporleder 2014</ref>, S.&#160;37, akzeptieren etwa eine Klassifizierung bei
+                  mehrfachen Labels als korrekt, wenn die erkannte Klasse zumindest zu einem der
+                  Label passt, während in der vorliegenden Studie angestrebt wurde, Texte mit
+                  mehrfachen Labels zu meiden. Eine Vergleichbarkeit der Studien leidet&#160;– wie so oft
+                  im Bereich der Digital Humanities&#160;– auch darunter, dass viele Publikationsorgane
+                  den Maximalumfang der Beiträge auf derart wenige Seiten einschränken, dass eine
+                  Dokumentation von Setting, Parametern etc. nicht ausreichend möglich ist. Solche
+                  Seiteneinschränkungen muten vor allem dort, wo Online-Publikationsformate gewählt
+                  werden, geradezu absurd an.</note>
+            </p>
+            </div>
+            <div type="subchapter">
+               <head>4.2 Setting: Tests mit F1-Wert und ARI</head>
+            
+            <p>Bei diesem Testverfahren gebe ich jeweils alle zehn Kontrollgruppentexte der
+               Nicht-Zielgattungen als Distraktortexte ins Korpus&#160;– es sei denn, es befindet
+               sich ein Autorenduplikat zu einem Text der Zielgattung darunter; in diesem Fall wird
+               dieser Distraktortext für den Test zur jeweiligen Zielgattung ersatzlos aus dem
+               Korpus genommen, so dass statt 40 nur 38 oder 39 Distraktortexte (Testreihe A) oder
+               statt 20 nur 18 oder 19 Distraktortexte (Testreihe B–D) verwendet werden. In einem
+               alternativen Versuch (nur Testreihen A und D) werden nur fünf zufällige
+               Distraktortexte je Nicht-Zielgattung (ohne Autorenduplikate zur Zielgattung)
+               verwendet. Weiterhin werden alle zehn Texte der Zielgattung ins Korpus gegeben. Für
+               alle möglichen Paare von jeweils zwei Texten des Korpus werden die Delta-Abstände
+               berechnet. Die ARI-Berechnung ist als <term type="dh">Zweiklassenspiel</term>
+               implementiert: Unterschieden wird zwischen der Zugehörigkeit zur Zielklasse und zur
+               Nicht-Zielklasse. Über die Klassenzugehörigkeit entscheidet dabei der niedrigste
+               Delta-Abstand. </p>
+            <p>Durchgeführt werden diese Tests ohne weitere Optimierungsmaßnahmen wie das
+               Eliminieren von Pronomina, jedoch mit Berücksichtigung der jeweiligen
+               Gute-Wörter-Liste und mit Z-Wert-Begrenzung auf 1,64.<note type="footnote"> Vgl. zur
+                  Z-Wert-Begrenzung <ref type="bibliography" target="#evert_et_al_burrows_2016">Evert et&#160;al. 2016</ref>; <ref type="bibliography" target="#dimpel_delta_2018b">Dimpel 2018b</ref>.</note> Die Z-Wert-Begrenzung
+               wird aufgrund der Annahme verwendet, dass textspezifisches Vokabular, das nicht
+               zugleich gattungsspezifisches Vokabular ist, auf diesem Weg mitunter aussortiert
+               werden könnte; zugleich könnten Nullwerte, die auf fehlenden Wörtern im Einzeltext
+               beruhen, weniger stark auf den Delta-Wert durchschlagen.</p>
+            <p>Bei der Auswertung ist zu bedenken, dass der ARI nicht direkt mit einer herkömmlichen
+               Erkennungsquote zu vergleichen ist. Bei dem oben beschriebenen Setting würde eine
+               Zufallsverteilung nicht eine Erkennungsquote von 0%, sondern von 20&#160;% bzw. 33%
+               ergeben. Eine Zufallsverteilung beim ARI-Wert ergibt den Wert 0; Clusterergebnisse,
+               die schlechter als eine Zufallsverteilung sind, führen zu negativen ARI-Werten. Dass
+               der ARI-Wert in vergleichbaren Konstellationen unter der Erkennungsquote liegt (wenn
+               man den Einfluss der False-Positives unberücksichtigt lässt), ist bereits durch den
+               abweichenden Wert für die Zufallsverteilung bedingt. Dieser Effekt verringert sich,
+               je mehr die Erkennungsquote gegen 100&#160;% und der ARI-Wert gegen 1 tendiert. </p>
+            <p>Neben dem ARI wird hier auch Erkennungsquote (Recall) und False-Positives-Quote
+               notiert, auf deren Basis die Precision ermittelt und der F1-Score für die
+               Zielgattungstexte ausgegeben wird. Für die Erkennungsquoten werden nur die
+               Delta-Abstände zwischen den Texten der Zielgattung zu allen Texten im Korpus
+               herangezogen; für die Nicht-Zielgattungstexte wird also keine Erkennungsquote
+               ermittelt&#160;– deren Clusterverhalten geht ohnehin in den ARI ein. Bei der
+               False-Positives-Quote werden die Nicht-Zielgattungstexte berücksichtigt, die zur
+               Zielgattung den niedrigsten Delta-Abstand aufweisen. Da hier ein Zielklassentext
+               gegen ein Korpus mit 9 Zielklassentexten und 38–40<note type="footnote"> Es sind 40
+                  Distraktortexte, wenn kein Autorduplikat in der Zielklasse vorliegt; ansonsten je
+                  nach Szenario ein oder zwei Distraktortexte weniger.</note> Distraktortexten (A)
+               bzw. 18–20 Distraktortexten (B–D) getestet wird, würde eine Zufallsverteilung bei ca.
+               18,4&#160;% (A) bzw. 31&#160;% (B–D) liegen. Die F1-Werte liegen durchwegs deutlich über den
+               ARI-Werten; bei letzteren gehen auch Anzahl und Clusteringverhalten der
+               Distraktortexte ein.</p>
+            </div>
+            </div>
+            <div type="chapter">
+               <head>5. Ergebnisse</head>
+            
+            <div type="subchapter">
+               <head>Testreihe A: ABE, BIL, GES, KOM, TRA</head>
+            </div>
+            <div type="subchapter">
+               <head>A1: Erkennungsquotentest mit fünf Vergleichstexten der Zielklasse</head>
+            
+            <table rend="rules" xml:id="gattungssignal_2022_t1">
+               <row>
+                  <cell/>
+                  <cell>200<hi rend="super">a</hi></cell>
+                  <cell>300<hi rend="super">a</hi></cell>
+                  <cell>400<hi rend="super">a</hi></cell>
+                  <cell/>
+               </row>
+               <row>
+                  <cell>Gute<hi rend="super">c</hi> &amp; ZWB<hi rend="super">d</hi></cell>
+                  <cell>57,3<hi rend="super">b</hi></cell>
+                  <cell>
+                     <hi rend="bold">63,4</hi>
+                  </cell>
+                  <cell>62,3</cell>
+                  <cell rows="4">Bag-of-Words 10.000</cell>
+               </row>
+               <row>
+                  <cell>Gute</cell>
+                  <cell>50,0</cell>
+                  <cell>53,4</cell>
+                  <cell>51,9</cell>
+                 
+               </row>
+               <row>
+                  <cell>ZWB</cell>
+                  <cell>48,2</cell>
+                  <cell>45,4</cell>
+                  <cell>44,0</cell>
+                  
+               </row>
+               <row>
+                  <cell>Basis<hi rend="super">e</hi></cell>
+                  <cell>45,4</cell>
+                  <cell>42,9</cell>
+                  <cell>44,7</cell>
+                  
+               </row>
+               <row>
+                  <cell>Gute &amp; ZWB</cell>
+                  <cell>51,6</cell>
+                  <cell>57,2</cell>
+                  <cell>56,8</cell>
+                  <cell rows="4">
+                     Volltexte
+                  </cell>
+               </row>
+               <row>
+                  <cell>Gute</cell>
+                  <cell>61,2</cell>
+                  <cell>60,8</cell>
+                  <cell>
+                     <hi rend="bold">65,6</hi>
+                  </cell>
+                  
+               </row>
+               <row>
+                  <cell>ZWB</cell>
+                  <cell>48,4</cell>
+                  <cell>48,0</cell>
+                  <cell>50,4</cell>
+                  
+               </row>
+               <row>
+                  <cell>Basis</cell>
+                  <cell>46,4</cell>
+                  <cell>44,0</cell>
+                  <cell>47,6</cell>
+                  
+               </row>
+               <row>
+                  <cell>Zufallsquote</cell>
+                  <cell>20,0</cell>
+                  <cell>20,0</cell>
+                  <cell>20,0</cell>
+                 
+               </row>
+               <row>
+               <cell cols="5">
+                  <hi rend="super">a</hi> Anzahl der MFWs, die verwendet werden<lb/>
+                  <hi rend="super">b</hi> Erkennungsquote in %<lb/>
+                  <hi rend="super">c</hi> Gute: Mit Gute-Wörter-Liste<lb/>
+                  <hi rend="super">d</hi> ZWB: Mit Z-Wert-Begrenzung auf 1,64<lb/>
+                  <hi rend="super">e</hi> Basis: Ohne Gute-Wörter-Liste und ohne Z-Wert-Begrenzung<lb/>
+               </cell>
+               </row>
+               <trailer xml:id="tab001">
+                  <ref type="intern" target="#tab1">Tab. 1</ref>: Test A1, Erkennungsquotentest mit fünf Vergleichstexten der Zielklasse, Test A1, ABE, BIL, GES, KOM, TRA. Beim Bag-of-Words-Test mit 10.000 MFWs werden die Texte 200 verschiedenen Bag-Sets pro Gattung zugelost und Mittelwerte gebildet.<ref type="graphic" target="#gattungssignal_2022_t1"/>
+               </trailer>
+               </table>
+               
+            <p>Die Guten Wörter führen zu einer deutlichen Verbesserung der Erkennungsquote. Der
+               Verbesserungseffekt ist am stärksten ausgeprägt beim Bag-of-Words-Verfahren mit
+               Z-Wert-Begrenzung; der höchste Wert insgesamt wird bei Volltexten und ohne
+               Z-Wert-Begrenzung erreicht. Im Vergleich zu Autorschaftsstudien liegen die Quoten
+               deutlich niedriger&#160;– dort werden Werte &gt;90&#160;% erreicht, selbst wenn sich nur ein
+               Text der Zielautorin&#160;/&#160;des Zielautors im Vergleichskorpus befindet.<note type="footnote"> Vgl.
+                  etwa <ref type="bibliography" target="#Büttner_et_al_delta_2017">Büttner et&#160;al. 2017</ref>.</note>
+            </p>
+            </div>
+            <div type="subchapter">
+               <head>A2: ARI-Test mit 4 × 10 Distraktortexten</head>
+            
+               <table rend="rules" xml:id="gattungssignal_2022_t2">
+                  <row>
+                     
+                     <cell>Volltexte</cell>
+                     
+                     <cell>200<hi rend="super">a</hi></cell>
+                     
+                     <cell>300<hi rend="super">a</hi></cell>
+                     
+                     <cell>400<hi rend="super">a</hi></cell>
+                     
+                  </row>
+                  <row>
+                     
+                     <cell>ARI<hi rend="super">c</hi> Gute<hi rend="super">d</hi> &amp; ZWB<hi rend="super">e</hi></cell>
+                     
+                     <cell>
+                        <hi rend="bold">0,34</hi><hi rend="super">b</hi>
+                     </cell>
+                     
+                     <cell>0,28</cell>
+                     
+                     <cell>0,26</cell>
+                     
+                  </row>
+                  <row>
+                     
+                     <cell>ARI Gute</cell>
+                     
+                     <cell>0,28</cell>
+                     
+                     <cell>0,26</cell>
+                     
+                     <cell>0,25</cell>
+                     
+                  </row>
+                  <row>
+                     
+                     <cell>ARI ZWB</cell>
+                     
+                     <cell>0,33</cell>
+                     
+                     <cell>0,31</cell>
+                     
+                     <cell>
+                        <hi rend="bold">0,34</hi>
+                     </cell>
+                     
+                  </row>
+                  <row>
+                     
+                     <cell>ARI Basis<hi rend="super">f</hi></cell>
+                     
+                     <cell>0,25</cell>
+                     
+                     <cell>0,2</cell>
+                     
+                     <cell>0,25</cell>
+                     
+                  </row>
+                  <row>
+                     
+                     <cell/>
+                     
+                     <cell/>
+                     
+                     <cell/>
+                     
+                     <cell/>
+                     
+                  </row>
+                  <row>
+                     
+                     <cell>F1<hi rend="super">g</hi> Gute &amp; ZWB</cell>
+                     
+                     <cell>
+                        <hi rend="bold">0,68</hi>
+                     </cell>
+                     
+                     <cell>0,64</cell>
+                     
+                     <cell>0,65</cell>
+                     
+                  </row>
+                  <row>
+                     
+                     <cell>F1 Gute</cell>
+                     
+                     <cell>0,66</cell>
+                     
+                     <cell>0,65</cell>
+                     
+                     <cell>0,66</cell>
+                     
+                  </row>
+                  <row>
+                     
+                     <cell>F1 ZWB</cell>
+                     
+                     <cell>0,66</cell>
+                     
+                     <cell>0,63</cell>
+                     
+                     <cell>0,66</cell>
+                     
+                  </row>
+                  <row>
+                     
+                     <cell>F1 Basis</cell>
+                     
+                     <cell>0,60</cell>
+                     
+                     <cell>0,56</cell>
+                     
+                     <cell>0,60</cell>
+                     
+                  </row>
+                  <row>
+                     
+                     <cell/>
+                     
+                     <cell/>
+                     
+                     <cell/>
+                     
+                     <cell/>
+                     
+                  </row>
+                  <row>
+                     
+                     <cell>EQ<hi rend="super">h</hi> Gute &amp; ZWB</cell>
+                     
+                     <cell>
+                        <hi rend="bold">58</hi>
+                     </cell>
+                     
+                     <cell>54</cell>
+                     
+                     <cell>46</cell>
+                     
+                  </row>
+                  <row>
+                     
+                     <cell>EQ Gute</cell>
+                     
+                     <cell>56</cell>
+                     
+                     <cell>56</cell>
+                     
+                     <cell>
+                        <hi rend="bold">58</hi>
+                     </cell>
+                     
+                  </row>
+                  <row>
+                     
+                     <cell>EQ ZWB</cell>
+                     
+                     <cell>54</cell>
+                     
+                     <cell>52</cell>
+                     
+                     <cell>43</cell>
+                     
+                  </row>
+                  <row>
+                     
+                     <cell>EQ Basis</cell>
+                     
+                     <cell>48</cell>
+                     
+                     <cell>44</cell>
+                     
+                     <cell>48</cell>
+                     
+                  </row>
+                  <row>
+                     
+                     <cell/>
+                     
+                     <cell/>
+                     
+                     <cell/>
+                     
+                     <cell/>
+                     
+                  </row>
+                  <row>
+                     
+                     <cell>FP<hi rend="super">i</hi> Gute &amp; ZWB</cell>
+                     
+                     <cell>12,4</cell>
+                     
+                     <cell>13,9</cell>
+                     
+                     <cell>16,0</cell>
+                     
+                  </row>
+                  <row>
+                     
+                     <cell>FP Gute</cell>
+                     
+                     <cell>14,5</cell>
+                     
+                     <cell>15,5</cell>
+                     
+                     <cell>18,1</cell>
+                     
+                  </row>
+                  <row>
+                     
+                     <cell>FP ZWB</cell>
+                     
+                     <cell>10,8</cell>
+                     
+                     <cell>11,9</cell>
+                     
+                     <cell>
+                        <hi rend="bold">9,8</hi>
+                     </cell>
+                     
+                  </row>
+                  <row>
+                     
+                     <cell>FP Basis</cell>
+                     
+                     <cell>12,9</cell>
+                     
+                     <cell>13,0</cell>
+                     
+                     <cell>12,5</cell>
+                     
+                  </row>
+                  <row>
+                     
+                     <cell/>
+                     
+                     <cell/>
+                     
+                     <cell/>
+                     
+                     <cell/>
+                     
+                  </row>
+                  <row>
+                     
+                     <cell>Diff<hi rend="super">j</hi> ARI</cell>
+                     
+                     <cell>0,09</cell>
+                     
+                     <cell>0,08</cell>
+                     
+                     <cell>0,01</cell>
+                     
+                  </row>
+                  <row>
+                     
+                     <cell>Diff EQ</cell>
+                     
+                     <cell>10,0</cell>
+                     
+                     <cell>10,0</cell>
+                     
+                     <cell>8,0</cell>
+                     
+                  </row>
+                  <row>
+                     
+                     <cell>Diff FP</cell>
+                     
+                     <cell>0,5</cell>
+                     
+                     <cell>-1,0</cell>
+                     
+                     <cell>-3,5</cell>
+                     
+                  </row>
+                  <row>
+                     <cell cols="4"><hi rend="super">a</hi> Anzahl der MFWs, die verwendet werden<lb/>
+                        <hi rend="super">b</hi> Erkennungsquote in %<lb/>
+                        <hi rend="super">c</hi> ARI: Adjusted Rand Index<lb/>
+                        <hi rend="super">d</hi> Gute: Mit Gute-Wörter-Liste <lb/>
+                        <hi rend="super">e</hi> ZWB: Mit Z-Wert-Begrenzung auf 1,64<lb/>
+                        <hi rend="super">f</hi> Basis: Ohne Gute-Wörter-Liste und ohne Z-Wert-Begrenzung<lb/>
+                        <hi rend="super">g</hi> F1: Precision und Recall kombiniert <lb/>
+                        <hi rend="super">h</hi> EQ: Erkennungsquote in&#160;% <lb/>
+                        <hi rend="super">i</hi> FP: False-Positives-Quote in&#160;% (niedriger ist besser)<lb/>
+                        <hi rend="super">j</hi> Diff: Differenzen zwischen Werten mit Gute-Wörter-Liste und mit Z-Wert-Begrenzung zum Basiswert
+                     </cell>
+                  </row>
+                    <trailer xml:id="tab002"><ref type="intern" target="#tab2">Tab. 2</ref>: Test A2, ARI-Test mit 4 × 10 Distraktortexten, ABE, BIL, GES, KOM, TRA.<ref type="graphic" target="#gattungssignal_2022_t2"/></trailer>
+               </table>
+            <p>Die besten ARI-Werte finden sich bei der Kombination der bevorzugten Verwendung von
+               Guten Wörtern mit der Z-Wert-Begrenzung bei 200 MFWs sowie bei den Werten mit
+               Z-Wert-Begrenzung. Die Guten Wörter begünstigen eine Verbesserung der
+               Erkennungsquote, die Z-Wert-Begrenzung führt zu einer besseren False-Positives-Quote
+               bei einer etwas niedrigeren Erkennungsquote. Bei der Kombination beider Techniken
+               verbessert sich bei 200 MFWs die False-Positives-Quote gegenüber dem Basiswert
+               leicht; die Erkennungsquote bleibt zugleich deutlich besser. Bei 200 MFWs verbessert
+               sich der ARI-Wert um 0,09 deutlich, jedoch insgesamt auf mäßigem Niveau. Bei 300 und
+               400 MFWs gehen Gute Wörter mit schlechterer Level-2-Differenz ein; zugleich
+               begünstigt ein größerer Vektor eine bessere Erkennung.</p>
+            <p>Hier ein Blick in die Einzelwerte für die Gattungen bei 200 MFWs mit Guten Wörtern
+               und Z-Wert-Begrenzung:</p>
+               <table rend="rules" xml:id="gattungssignal_2022_t3">
+                  <row>
+                     
+                     <cell>200 MFWs</cell>
+                     
+                     <cell>ARI<hi rend="super">a</hi></cell>
+                     
+                     <cell>F1<hi rend="super">b</hi></cell>
+                     
+                     <cell>EQ<hi rend="super">c</hi></cell>
+                     
+                     <cell>FP<hi rend="super">d</hi></cell>
+                     
+                  </row>
+                  <row>
+                     
+                     <cell>ABE</cell>
+                     
+                     <cell>0,64</cell>
+                     
+                     <cell>0,81</cell>
+                     
+                     <cell>70</cell>
+                     
+                     <cell>2,6</cell>
+                     
+                  </row>
+                  <row>
+                     
+                     <cell>BIL</cell>
+                     
+                     <cell>0,28</cell>
+                     
+                     <cell>0,68</cell>
+                     
+                     <cell>60</cell>
+                     
+                     <cell>15,4</cell>
+                     
+                  </row>
+                  <row>
+                     
+                     <cell>GES</cell>
+                     
+                     <cell>0,11</cell>
+                     
+                     <cell>0,50</cell>
+                     
+                     <cell>40</cell>
+                     
+                     <cell>18,4</cell>
+                     
+                  </row>
+                  <row>
+                     
+                     <cell>KOM</cell>
+                     
+                     <cell>0,38</cell>
+                     
+                     <cell>0,71</cell>
+                     
+                     <cell>60</cell>
+                     
+                     <cell>10,0</cell>
+                     
+                  </row>
+                  <row>
+                     
+                     <cell>TRA</cell>
+                     
+                     <cell>0,27</cell>
+                     
+                     <cell>0,68</cell>
+                     
+                     <cell>60</cell>
+                     
+                     <cell>15,8</cell>
+                     
+                  </row>
+                  <row>
+                     <cell cols="5"><hi rend="super">a</hi> ARI: Adjusted Rand Index<lb/>
+                        <hi rend="super">b</hi> F1: Precision und Recall kombiniert<lb/>
+                        <hi rend="super">c</hi> EQ: Erkennungsquote in %<lb/>
+                        <hi rend="super">d</hi> FP: False-Positives-Quote in&#160;% (niedriger ist besser)<lb/>
+                     </cell>
+                  </row>
+                  <trailer xml:id="tab003"><ref type="intern" target="#tab3">Tab. 3</ref>: Test A2, ARI-Test mit 4 × 10 Distraktortexten, Einzelwerte, ABE,
+                        BIL, GES, KOM, TRA.<ref type="graphic" target="#gattungssignal_2022_t3"/></trailer>
+               </table>
+            <p>Der Gesellschaftsroman erweist sich als problematisch&#160;– mit niedriger Erkennungsquote
+               und hoher False-Positives-Rate. Auch Bildungsromane und Tragödien zeigen eine hohe
+               False-Positives-Rate. Ãœberraschend niedrig ist die False-Positives-Rate beim
+               Abenteuerroman, der insgesamt recht gut erkannt werden kann.<note type="footnote">
+                  Eine ähnliche Tendenz beobachten <ref type="bibliography" target="#hettinger_et_al_classification_2016a">Hettinger et&#160;al. 2016a</ref>, S. 160.</note>
+            </p>
+            <p>Die Bag-of-Words-Technik (hier mit 10.000 Wortformen) führt zu einer Verbesserung der
+               Erkennungsquote bei 300 und 400 MFWs, jedoch auch zu mehr False-Positives, so dass
+               die ARI-Werte etwas schlechter sind. Hier nur die Daten mit Guten Wörtern und
+               Z-Wert-Begrenzung für alle fünf Gattungen:</p>
+               <table rend="rules" xml:id="gattungssignal_2022_t4">
+                  <row>
+                     
+                     <cell>MFWs</cell>
+                     
+                     <cell>200</cell>
+                     
+                     <cell>300</cell>
+                     
+                     <cell>400</cell>
+                     
+                  </row>
+                  <row>
+                     
+                     <cell>ARI<hi rend="super">a</hi></cell>
+                     
+                     <cell>0,28</cell>
+                     
+                     <cell>0,31</cell>
+                     
+                     <cell>0,3</cell>
+                     
+                  </row>
+                  <row>
+                     
+                     <cell>F1<hi rend="super">b</hi></cell>
+                     
+                     <cell>0,64</cell>
+                     
+                     <cell>0,69</cell>
+                     
+                     <cell>0,69</cell>
+                     
+                  </row>
+                  <row>
+                     
+                     <cell>EQ<hi rend="super">c</hi></cell>
+                     
+                     <cell>53,3</cell>
+                     
+                     <cell>61,2</cell>
+                     
+                     <cell>61,2</cell>
+                     
+                  </row>
+                  <row>
+                     
+                     <cell>FP<hi rend="super">d</hi></cell>
+                     
+                     <cell>13,9</cell>
+                     
+                     <cell>15,0</cell>
+                     
+                     <cell>15,7</cell>
+                     
+                  </row>
+                  <row><cell cols="4"><hi rend="super">a</hi> ARI: Adjusted Rand Index<lb/>
+                     <hi rend="super">b</hi> F1: Precision und Recall kombiniert<lb/>
+                        <hi rend="super">c</hi> EQ: Erkennungsquote in %<lb/>
+                           <hi rend="super">d</hi> FP: False-Positives-Quote in&#160;% (niedriger ist besser)
+                  </cell></row>
+
+                  <trailer xml:id="tab004"><ref type="intern" target="#tab4">Tab. 4</ref>: Test A2, ARI-Test mit 4 × 10 Distraktortexten, Bag-of-Words, ABE,
+                     BIL, GES, KOM, TRA. <ref type="graphic" target="#gattungssignal_2022_t4"/></trailer>
+               </table>
+            </div>
+            <div type="subchapter">
+               <head>A3: ARI-Test mit 4 × 5 Distraktortexten</head>
+               <table rend="rules" xml:id="gattungssignal_2022_t5">
+                     <row>
+                        <cell>Volltexte</cell>
+                        <cell>200</cell>
+                        <cell>300</cell>
+                        <cell>400</cell>
+                     </row>
+                     <row>
+                        <cell>ARI<hi rend="super">a</hi> Gute<hi rend="super">b</hi> &amp; ZWB<hi rend="super">c</hi></cell>
+                        <cell>0,31</cell>
+                        <cell>
+                           <hi rend="bold">0,32</hi>
+                        </cell>
+                        <cell>0,30</cell>
+                     </row>
+                     <row>
+                        <cell>ARI Gute</cell>
+                        <cell>0,29</cell>
+                        <cell>0,29</cell>
+                        <cell>0,28</cell>
+                     </row>
+                     <row>
+                        <cell>ARI ZWB</cell>
+                        <cell>0,24</cell>
+                        <cell>0,25</cell>
+                        <cell>0,3</cell>
+                     </row>
+                     <row>
+                        <cell>ARI Basis<hi rend="super">d</hi></cell>
+                        <cell>0,19</cell>
+                        <cell>0,16</cell>
+                        <cell>0,19</cell>
+                     </row>
+                     <row>
+                        <cell/>
+                        <cell/>
+                        <cell/>
+                        <cell/>
+                     </row>
+                     <row>
+                        <cell>F1<hi rend="super">e</hi> Gute &amp; ZWB</cell>
+                        <cell>0,76</cell>
+                        <cell>0,76</cell>
+                        <cell>
+                           <hi rend="bold">0,77</hi>
+                        </cell>
+                     </row>
+                     <row>
+                        <cell>F1 Gute</cell>
+                        <cell>0,76</cell>
+                        <cell>0,76</cell>
+                        <cell>0,76</cell>
+                     </row>
+                     <row>
+                        <cell>F1 ZWB</cell>
+                        <cell>0,70</cell>
+                        <cell>0,70</cell>
+                        <cell>0,73</cell>
+                     </row>
+                     <row>
+                        <cell>F1 Basis</cell>
+                        <cell>0,65</cell>
+                        <cell>0,62</cell>
+                        <cell>0,65</cell>
+                     </row>
+                     <row>
+                        <cell/>
+                        <cell/>
+                        <cell/>
+                        <cell/>
+                     </row>
+                     <row>
+                        <cell>EQ<hi rend="super">f</hi> Gute &amp; ZWB</cell>
+                        <cell>74,00</cell>
+                        <cell>73,80</cell>
+                        <cell>76,00</cell>
+                     </row>
+                     <row>
+                        <cell>EQ Gute</cell>
+                        <cell>74,6</cell>
+                        <cell>74,6</cell>
+                        <cell>
+                           <hi rend="bold">77</hi>
+                        </cell>
+                     </row>
+                     <row>
+                        <cell>EQ ZWB</cell>
+                        <cell>63,8</cell>
+                        <cell>64,6</cell>
+                        <cell>68</cell>
+                     </row>
+                     <row>
+                        <cell>EQ Basis</cell>
+                        <cell>56,8</cell>
+                        <cell>54,6</cell>
+                        <cell>57</cell>
+                     </row>
+                     <row>
+                        <cell/>
+                        <cell/>
+                        <cell/>
+                        <cell/>
+                     </row>
+                     <row>
+                        <cell>FP<hi rend="super">g</hi> Gute &amp; ZWB</cell>
+                        <cell>20,20</cell>
+                        <cell>19,90</cell>
+                        <cell>22,10</cell>
+                     </row>
+                     <row>
+                        <cell>FP Gute</cell>
+                        <cell>21,8</cell>
+                        <cell>22,6</cell>
+                        <cell>24,8</cell>
+                     </row>
+                     <row>
+                        <cell>FP ZWB</cell>
+                        <cell>19,5</cell>
+                        <cell>20,3</cell>
+                        <cell>
+                           <hi rend="bold">18</hi>
+                        </cell>
+                     </row>
+                     <row>
+                        <cell>FP Basis</cell>
+                        <cell>18,9</cell>
+                        <cell>20,7</cell>
+                        <cell>19,4</cell>
+                     </row>
+                     <row>
+                        <cell/>
+                        <cell/>
+                        <cell/>
+                        <cell/>
+                     </row>
+                     <row>
+                        <cell>Diff<hi rend="super">h</hi> ARI</cell>
+                        <cell>0,12</cell>
+                        <cell>0,16</cell>
+                        <cell>0,11</cell>
+                     </row>
+                     <row>
+                        <cell>Diff EQ</cell>
+                        <cell>17,20</cell>
+                        <cell>19,20</cell>
+                        <cell>19,00</cell>
+                     </row>
+                     <row>
+                        <cell>Diff FP</cell>
+                        <cell>-1,30</cell>
+                        <cell>0,80</cell>
+                        <cell>-2,70</cell>
+                     </row>
+                  <row>
+                     <cell cols="4"><hi rend="super">a</hi> ARI: Adjusted Rand Index<lb/>
+                        <hi rend="super">b</hi> Gute: Mit Gute-Wörter-Liste<lb/>
+                        <hi rend="super">c</hi> ZWB: Mit Z-Wert-Begrenzung auf 1,64<lb/>
+                        <hi rend="super">d</hi> Basis: Ohne Gute-Wörter-Liste und ohne Z-Wert-Begrenzung<lb/>
+                        <hi rend="super">e</hi> F1: Precision und Recall kombiniert<lb/>
+                        <hi rend="super">f</hi> EQ: Erkennungsquote in %<lb/>
+                        <hi rend="super">g</hi> FP: False-Positives-Quote in&#160;% (niedriger ist besser)<lb/>
+                        <hi rend="super">h</hi> Diff: Differenzen zwischen Werten mit Gute-Wörter-Liste und mit Z-Wert-Begrenzung zum Basiswert
+                     </cell>
+                  </row>
+                  <trailer xml:id="tab005"><ref type="intern" target="#tab5">Tab. 5</ref>: Test A3, ARI-Test mit 4 × 5 Distraktortexten, ABE, BIL, GES, KOM,
+                     TRA. <ref target="#gattungssignal_2022_t5"/></trailer>                
+               </table>
+            <p>Gegenüber dem Test mit 4 × 10 Distraktortexten geht eine Verbesserung der
+               Erkennungsquote mit einer Verschlechterung der False-Positives-Quote einher. Bei 200
+               MFWs ist die Verschlechterung der False-Positives-Quote nur leicht, bei 400 MFWs
+               deutlich ausgeprägt. </p>
+            </div>
+            <div type="subchapter">
+               <head>Test B: ABE, BIL, KOM</head>
+               <table rend="rules" xml:id="gattungssignal_2022_t6">
+                  <row>
+                     
+                     <cell>Volltexte</cell>
+                     
+                     <cell>200</cell>
+                     
+                     <cell>300</cell>
+                     
+                     <cell>400</cell>
+                     
+                  </row>
+                  <row>
+                     
+                     <cell>ARI<hi rend="super">a</hi> Gute<hi rend="super">b</hi> &amp; ZWB<hi rend="super">c</hi></cell>
+                     
+                     <cell>0,62</cell>
+                     
+                     <cell>
+                        <hi rend="bold">0,68</hi>
+                     </cell>
+                     
+                     <cell>0,59</cell>
+                     
+                  </row>
+                  <row>
+                     
+                     <cell>ARI Gute</cell>
+                     
+                     <cell>0,51</cell>
+                     
+                     <cell>0,62</cell>
+                     
+                     <cell>0,5</cell>
+                     
+                  </row>
+                  <row>
+                     
+                     <cell>ARI ZWB</cell>
+                     
+                     <cell>0,5</cell>
+                     
+                     <cell>0,46</cell>
+                     
+                     <cell>
+                        <hi rend="bold">0,68</hi>
+                     </cell>
+                     
+                  </row>
+                  <row>
+                     
+                     <cell>ARI Basis<hi rend="super">d</hi></cell>
+                     
+                     <cell>0,5</cell>
+                     
+                     <cell>0,46</cell>
+                     
+                     <cell>0,56</cell>
+                     
+                  </row>
+                  <row>
+                     
+                     <cell/>
+                     
+                     <cell/>
+                     
+                     <cell/>
+                     
+                     <cell/>
+                     
+                  </row>
+                  <row>
+                     
+                     <cell>F1<hi rend="super">e</hi> Gute &amp; ZWB</cell>
+                     
+                     <cell>0,88</cell>
+                     
+                     <cell>
+                        <hi rend="bold">0,91</hi>
+                     </cell>
+                     
+                     <cell>0,89</cell>
+                     
+                  </row>
+                  <row>
+                     
+                     <cell>F1 Gute</cell>
+                     
+                     <cell>0,83</cell>
+                     
+                     <cell>0,89</cell>
+                     
+                     <cell>0,86</cell>
+                     
+                  </row>
+                  <row>
+                     
+                     <cell>F1 ZWB</cell>
+                     
+                     <cell>0,81</cell>
+                     
+                     <cell>0,79</cell>
+                     
+                     <cell>0,90</cell>
+                     
+                  </row>
+                  <row>
+                     
+                     <cell>F1 Basis</cell>
+                     
+                     <cell>0,81</cell>
+                     
+                     <cell>0,79</cell>
+                     
+                     <cell>0,84</cell>
+                     
+                  </row>
+                  <row>
+                     
+                     <cell/>
+                     
+                     <cell/>
+                     
+                     <cell/>
+                     
+                     <cell/>
+                     
+                  </row>
+                  <row>
+                     
+                     <cell>EQ<hi rend="super">f</hi> Gute &amp; ZWB</cell>
+                     
+                     <cell>86,7</cell>
+                     
+                     <cell>
+                        <hi rend="bold">90,0</hi>
+                     </cell>
+                     
+                     <cell>
+                        <hi rend="bold">90,0</hi>
+                     </cell>
+                     
+                  </row>
+                  <row>
+                     
+                     <cell>EQ Gute</cell>
+                     
+                     <cell>80,0</cell>
+                     
+                     <cell>
+                        <hi rend="bold">90,0</hi>
+                     </cell>
+                     
+                     <cell>
+                        <hi rend="bold">90,0</hi>
+                     </cell>
+                     
+                  </row>
+                  <row>
+                     
+                     <cell>EQ ZWB</cell>
+                     
+                     <cell>76,7</cell>
+                     
+                     <cell>73,3</cell>
+                     
+                     <cell>86,7</cell>
+                     
+                  </row>
+                  <row>
+                     
+                     <cell>EQ Basis</cell>
+                     
+                     <cell>76,7</cell>
+                     
+                     <cell>73,3</cell>
+                     
+                     <cell>80,0</cell>
+                     
+                  </row>
+                  <row>
+                     
+                     <cell/>
+                     
+                     <cell/>
+                     
+                     <cell/>
+                     
+                     <cell/>
+                     
+                  </row>
+                  <row>
+                     
+                     <cell>FP<hi rend="super">g</hi> Gute &amp; ZWB</cell>
+                     
+                     <cell>10,0</cell>
+                     
+                     <cell>8,3</cell>
+                     
+                     <cell>13,3</cell>
+                     
+                  </row>
+                  <row>
+                     
+                     <cell>FP Gute</cell>
+                     
+                     <cell>13,3</cell>
+                     
+                     <cell>11,7</cell>
+                     
+                     <cell>20,0</cell>
+                     
+                  </row>
+                  <row>
+                     
+                     <cell>FP ZWB</cell>
+                     
+                     <cell>11,7</cell>
+                     
+                     <cell>13,3</cell>
+                     
+                     <cell>
+                        <hi rend="bold">6,7</hi>
+                     </cell>
+                     
+                  </row>
+                  <row>
+                     
+                     <cell>FP Basis</cell>
+                     
+                     <cell>11,7</cell>
+                     
+                     <cell>13,3</cell>
+                     
+                     <cell>10,0</cell>
+                     
+                  </row>
+                  <row>
+                     
+                     <cell/>
+                     
+                     <cell/>
+                     
+                     <cell/>
+                     
+                     <cell/>
+                     
+                  </row>
+                  <row>
+                     
+                     <cell>Diff<hi rend="super">h</hi> ARI</cell>
+                     
+                     <cell>0,12</cell>
+                     
+                     <cell>0,22</cell>
+                     
+                     <cell>0,03</cell>
+                     
+                  </row>
+                  <row>
+                     
+                     <cell>Diff EQ</cell>
+                     
+                     <cell>10,0</cell>
+                     
+                     <cell>16,7</cell>
+                     
+                     <cell>10,0</cell>
+                     
+                  </row>
+                  <row>
+                     
+                     <cell>Diff FP</cell>
+                     
+                     <cell>1,7</cell>
+                     
+                     <cell>5,0</cell>
+                     
+                     <cell>-3,3</cell>
+                     
+                  </row>
+                  <row>
+                     <cell cols="4"><hi rend="super">a</hi> ARI: Adjusted Rand Index<lb/>
+                        <hi rend="super">b</hi> Gute: Mit Gute-Wörter-Liste<lb/>
+                        <hi rend="super">c</hi> ZWB: Mit Z-Wert-Begrenzung auf 1,64<lb/>
+                        <hi rend="super">d</hi> Basis: Ohne Gute-Wörter-Liste und ohne Z-Wert-Begrenzung<lb/>
+                        <hi rend="super">e</hi> F1: Precision und Recall kombiniert<lb/>
+                        <hi rend="super">f</hi> EQ: Erkennungsquote in %<lb/>
+                        <hi rend="super">g</hi> FP: False-Positives-Quote in&#160;% (niedriger ist besser)<lb/>
+                        <hi rend="super">h</hi> Diff: Differenzen zwischen Werten mit Gute-Wörter-Liste und mit Z-Wert-Begrenzung zum Basiswert</cell>
+                  </row>
+                  <trailer xml:id="tab006"><ref type="intern" target="#tab6">Tab. 6</ref>: Test B, ARI-Test mit 2 × 10 Distraktortexten, ABE, BIL, KOM. <ref type="graphic" target="#gattungssignal_2022_t6"/></trailer>
+               </table>
+            <p>Bei diesem Test bleibt der schwer unterscheidbare Gesellschaftsroman außen vor. Die
+               ARI-Werte verbessern sich in der Zeile ›Gute &amp; ZWB‹ auf ein ordentliches Niveau,
+               der F1-Wert kommt bei 300 MFWs auf ein gutes Niveau. Wiederum führen die Guten Wörter
+               zu besseren Erkennungsquoten und die Z-Wert-Begrenzung zu besseren
+               False-Positives-Quoten. In den Gattungseinzelwerten (hier nicht abgedruckt) ergibt
+               sich eine optimale Erkennung der Komödie (ARI=1 bei 200–400 MFWs mit Guten Wörtern
+               und Z-Wert-Begrenzung).</p>
+            </div>
+            <div type="subchapter">
+               <head>Test C: ABE, KOM, TRA</head>
+            
+            <p>Hier wird unter den Romansubgenres nur der besser unterscheidbare Abenteuerroman
+               einbezogen. Test C ist der einzige Test in dieser Studie, in der nicht verschiedene
+               Romansubgenres beteiligt sind&#160;– hier kann man am ehesten von drei verschiedenen
+               Gattungen sprechen.</p>
+               <table rend="rules" xml:id="gattungssignal_2022_t7">
+                  <row>
+                     <cell>Volltexte</cell>
+                     <cell>200</cell>
+                     <cell>300</cell>
+                     <cell>400</cell>
+                  </row>
+                  <row>
+                     <cell>ARI<hi rend="super">a</hi> Gute<hi rend="super">b</hi> &amp; ZWB<hi rend="super">c</hi></cell>
+                     <cell>0,45</cell>
+                     <cell>
+                        <hi rend="bold">0,53</hi>
+                     </cell>
+                     <cell>0,44</cell>
+                  </row>
+                  <row>
+                     <cell>ARI Gute</cell>
+                     <cell>0,47</cell>
+                     <cell>0,43</cell>
+                     <cell>0,38</cell>
+                  </row>
+                  <row>
+                     <cell>ARI ZWB</cell>
+                     <cell>0,5</cell>
+                     <cell>0,45</cell>
+                     <cell>0,5</cell>
+                  </row>
+                  <row>
+                     <cell>ARI Basis<hi rend="super">d</hi></cell>
+                     <cell>0,45</cell>
+                     <cell>0,37</cell>
+                     <cell>0,41</cell>
+                  </row>
+                  <row>
+                     <cell/>
+                     <cell/>
+                     <cell/>
+                     <cell/>
+                  </row>
+                  <row>
+                     <cell>F1<hi rend="super">e</hi> Gute &amp; ZWB</cell>
+                     <cell>0,78</cell>
+                     <cell>
+                        <hi rend="bold">0,85</hi>
+                     </cell>
+                     <cell>0,80</cell>
+                  </row>
+                  <row>
+                     <cell>F1 Gute</cell>
+                     <cell>0,80</cell>
+                     <cell>0,80</cell>
+                     <cell>0,75</cell>
+                  </row>
+                  <row>
+                     <cell>F1 ZWB</cell>
+                     <cell>0,81</cell>
+                     <cell>0,78</cell>
+                     <cell>0,81</cell>
+                  </row>
+                  <row>
+                     <cell>F1 Basis</cell>
+                     <cell>0,78</cell>
+                     <cell>0,72</cell>
+                     <cell>0,75</cell>
+                  </row>
+                  <row>
+                     <cell/>
+                     <cell/>
+                     <cell/>
+                     <cell/>
+                  </row>
+                  <row>
+                     <cell>EQ<hi rend="super">f</hi> Gute &amp; ZWB</cell>
+                     <cell>73,33</cell>
+                     <cell>
+                        <hi rend="bold">83,33</hi>
+                     </cell>
+                     <cell>80</cell>
+                  </row>
+                  <row>
+                     <cell>EQ Gute</cell>
+                     <cell>76,67</cell>
+                     <cell>80</cell>
+                     <cell>73,33</cell>
+                  </row>
+                  <row>
+                     <cell>EQ ZWB</cell>
+                     <cell>76,67</cell>
+                     <cell>73,33</cell>
+                     <cell>76,67</cell>
+                  </row>
+                  <row>
+                     <cell>EQ Basis</cell>
+                     <cell>73,33</cell>
+                     <cell>66,67</cell>
+                     <cell>70,0</cell>
+                  </row>
+                  <row>
+                     <cell/>
+                     <cell/>
+                     <cell/>
+                     <cell/>
+                  </row>
+                  <row>
+                     <cell>FP<hi rend="super">g</hi> Gute &amp; ZWB</cell>
+                     <cell>13,68</cell>
+                     <cell>13,68</cell>
+                     <cell>18,86</cell>
+                  </row>
+                  <row>
+                     <cell>FP Gute</cell>
+                     <cell>15,53</cell>
+                     <cell>20,7</cell>
+                     <cell>22,37</cell>
+                  </row>
+                  <row>
+                     <cell>FP ZWB</cell>
+                     <cell>
+                        <hi rend="bold">11,93</hi>
+                     </cell>
+                     <cell>13,68</cell>
+                     <cell>12,02</cell>
+                  </row>
+                  <row>
+                     <cell>FP Basis</cell>
+                     <cell>13,68</cell>
+                     <cell>17,28</cell>
+                     <cell>15,61</cell>
+                  </row>
+                  <row>
+                     <cell/>
+                     <cell/>
+                     <cell/>
+                     <cell/>
+                  </row>
+                  <row>
+                     <cell>Diff<hi rend="super">h</hi> ARI</cell>
+                     <cell>0,00</cell>
+                     <cell>0,16</cell>
+                     <cell>0,03</cell>
+                  </row>
+                  <row>
+                     <cell>Diff EQ</cell>
+                     <cell>0,00</cell>
+                     <cell>16,66</cell>
+                     <cell>10,00</cell>
+                  </row>
+                  <row>
+                     <cell>Diff FP</cell>
+                     <cell>0,00</cell>
+                     <cell>3,60</cell>
+                     <cell>-3,25</cell>
+                  </row>
+                  <row>
+                     <cell cols="4"><hi rend="super">a</hi> ARI: Adjusted Rand Index<lb/>
+                        <hi rend="super">b</hi> Gute: Mit Gute-Wörter-Liste<lb/>
+                        <hi rend="super">c</hi> ZWB: Mit Z-Wert-Begrenzung auf 1,64<lb/>
+                        <hi rend="super">d</hi> Basis: Ohne Gute-Wörter-Liste und ohne Z-Wert-Begrenzung<lb/>
+                        <hi rend="super">e</hi> F1: Precision und Recall kombiniert<lb/>
+                        <hi rend="super">f</hi> EQ: Erkennungsquote in %<lb/>
+                        <hi rend="super">g</hi> FP: False-Positives-Quote in&#160;% (niedriger ist besser)<lb/>
+                        <hi rend="super">h</hi> Diff: Differenzen zwischen Werten mit Gute-Wörter-Liste und mit Z-Wert-Begrenzung zum Basiswert</cell>
+                  </row>
+                  <trailer xml:id="tab007"><ref type="intern" target="#tab7">Tab. 7</ref>: Test C, ARI-Test mit 2 × 10 Distraktortexten, ABE, KOM, TRA. <ref type="graphic" target="#gattungssignal_2022_t7"/></trailer>
+               </table>
+            <p>Bei den ARI-Werten wird hier ein Niveau erreicht, das zwischen dem ordentlichen
+               Niveau der Testreihe B (ABE, BIL, KOM) und dem mäßigen Niveau der Testreihe A liegt.
+               Wie bislang führt auch hier die Z-Wert-Begrenzung zu einer Verbesserung bei den
+               False-Positives und die Gute-Wörter-Technik zu einer Verbesserung der
+               Erkennungsquote. </p>
+            <p>Auch hier setzen sich die Durchschnittswerte aus stark schwankenden Einzelwerten
+               zusammen: Während der Abenteuerroman sehr gut clustert, sind die Daten bei den
+               Tragödien ausgesprochen schlecht. </p>
+            <table rend="rules" xml:id="gattungssignal_2022_t8">
+               <row>
+                  <cell>ARI</cell>
+                  <cell>200</cell>
+                  <cell>300</cell>
+                  <cell>400</cell>
+               </row>
+               <row>
+                  <cell>ABE</cell>
+                  <cell>1</cell>
+                  <cell>1</cell>
+                  <cell>1</cell>
+               </row>
+               <row>
+                  <cell>KOM</cell>
+                  <cell>0,26</cell>
+                  <cell>0,43</cell>
+                  <cell>0,26</cell>
+               </row>
+               <row>
+                  <cell>TRA</cell>
+                  <cell>0,11</cell>
+                  <cell>0,17</cell>
+                  <cell>0,06</cell>
+               </row>
+               <trailer xml:id="tab008"><ref type="intern" target="#tab8">Tab. 8</ref>: Test C, ARI-Test mit 2 × 10 Distraktortexten, Einzelwerte, ABE,
+              KOM, TRA (Volltexte, mit Guten Wörtern und Z-Wert-Begrenzung).<ref type="graphic" target="#gattungssignal_2022_t8"/></trailer>
+            </table>
+            </div>
+            <div type="subchapter">
+               <head>Test D: ABE, BIL, GES</head>
+            
+            <p>Anders als in den Testreihen A–C werden hier keine verschiedenen Gattungen, sondern
+               lediglich Romansubgenres untersucht. Dies hat den Vorteil, dass dabei die teils
+               kurzen Komödien und Tragödien gemieden werden können. Das Bag-of-Words-Verfahren
+               kommt hier ohne Zurücklegen aus; ein weiterer Test (D4) mit einem größeren
+               MFW-Bereich wird dadurch möglich. </p>
+            </div>
+            <div type="subchapter">
+               <head>D1: Erkennungsquotentest mit fünf Vergleichstexten der Zielklasse</head>
+            
+            <table rend="rules" xml:id="gattungssignal_2022_t9">
+               <row>
+                  <cell/>
+                  <cell>200</cell>
+                  <cell>300</cell>
+                  <cell>400</cell>
+                  <cell/>
+               </row>
+               <row>
+                  <cell>Gute<hi rend="super">a</hi> &amp; ZWB<hi rend="super">b</hi></cell>
+                  <cell>
+                     <hi rend="bold">51,2</hi>
+                  </cell>
+                  <cell>51,3</cell>
+                  <cell>
+                     <hi rend="bold">61,6</hi>
+                  </cell>
+                  <cell rows="3">Bag-of-Words 10.000</cell>
+               </row>
+               <row>
+                  <cell>Gute</cell>
+                  <cell>50,3</cell>
+                  <cell>50,9</cell>
+                  <cell>60,3</cell>
+                  
+               </row>
+               <row>
+                  <cell>Basis<hi rend="super">c</hi></cell>
+                  <cell>35,7</cell>
+                  <cell>44,9</cell>
+                  <cell>41,2</cell>
+                  
+               </row>
+               <row>
+                  <cell>Gute &amp; ZWB</cell>
+                  <cell>45,3</cell>
+                  <cell>45,3</cell>
+                  <cell>58,7</cell>
+                  <cell rows="3">Volltexte</cell>
+               </row>
+               <row>
+                  <cell>Gute</cell>
+                  <cell>42,7</cell>
+                  <cell>56,7</cell>
+                  <cell>49,3</cell>
+                 
+               </row>
+               <row>
+                  <cell>Basis</cell>
+                  <cell>34,7</cell>
+                  <cell>47,3</cell>
+                  <cell>36,7</cell>
+                 
+               </row>
+               <row>
+                  <cell>Zufallsquote</cell>
+                  <cell>33,3</cell>
+                  
+               </row> 
+               <row>
+                  <cell cols="5"><hi rend="super">a</hi> Gute: Mit Gute-Wörter-Liste<lb/>
+                     <hi rend="super">b</hi> ZWB: Mit Z-Wert-Begrenzung auf 1,64<lb/>
+                     <hi rend="super">c</hi> Basis: Ohne Gute-Wörter-Liste und ohne Z-Wert-Begrenzung
+                  </cell>
+               </row>
+               <trailer xml:id="tab009"><ref type="intern" target="#tab9">Tab. 9</ref>: Test D1, Erkennungsquotentest mit fünf Vergleichstexten der
+   Zielklasse, ABE, BIL, GES.<ref type="graphic" target="#gattungssignal_2022_t9"/></trailer>
+            </table>
+            <p>Die Erkennungsquote mit den Optimierungstechniken ist beim Bag-of-Words-Verfahren
+               etwas besser als mit Volltexten; bei <ref type="intern" target="#hd8">Test A</ref> war
+               jedoch zu beobachten, dass diese Verbesserung mit einer Verschlechterung der
+               False-Positives-Quote einherging. Die Werte sind insgesamt etwas schlechter als die
+               Erkennungsquoten in der folgenden Tabelle beim ARI-Test; die Bag-of-Words-Tests
+               ergeben etwas höhere Werte. Während im ARI-Setting neun Zielklassentexte und 20
+               Distraktortexte zum Abgleich zur Verfügung stehen, werden hier fünf Zielklassentexte
+               und 10 Distraktortexte verwendet. Die Zufallsquote liegt beim ARI-Setting bei 31%,
+               hier bei 33,3%, also in einer ähnlichen Größenordnung. Als These, die die niedrigeren
+               Werte in diesem Setting erklären könnte, will ich die Überlegung notieren, dass die
+               Gattungserkennung bei einem größeren Korpus besser funktionieren könnte, da hier
+               Einzeltextspezifika weniger Gewicht haben könnten.</p>
+            </div>
+            <div type="subchapter">
+               <head>D2: ARI-Test mit 2 × 10 Distraktortexten</head>
+               <table rend="rules" xml:id="gattungssignal_2022_t10">
+                  <row>
+                     
+                     <cell>Volltexte</cell>
+                     
+                     <cell>200</cell>
+                     
+                     <cell>300</cell>
+                     
+                     <cell>400</cell>
+                     
+                  </row>
+                  <row>
+                     
+                     <cell>ARI<hi rend="super">a</hi> Gute<hi rend="super">b</hi> &amp; ZWB<hi rend="super">c</hi></cell>
+                     
+                     <cell>
+                        <hi rend="bold">0,22</hi>
+                     </cell>
+                     
+                     <cell>0,12</cell>
+                     
+                     <cell>0,2</cell>
+                     
+                  </row>
+                  <row>
+                     
+                     <cell>ARI Gute</cell>
+                     
+                     <cell>0,16</cell>
+                     
+                     <cell>0,06</cell>
+                     
+                     <cell>0,12</cell>
+                     
+                  </row>
+                  <row>
+                     
+                     <cell>ARI ZWB</cell>
+                     
+                     <cell>0,06</cell>
+                     
+                     <cell>0,04</cell>
+                     
+                     <cell>0,13</cell>
+                     
+                  </row>
+                  <row>
+                     
+                     <cell>ARI Basis<hi rend="super">d</hi></cell>
+                     
+                     <cell>0,04</cell>
+                     
+                     <cell>0,04</cell>
+                     
+                     <cell>0,07</cell>
+                     
+                  </row>
+                  <row>
+                     
+                     <cell/>
+                     
+                     <cell/>
+                     
+                     <cell/>
+                     
+                     <cell/>
+                     
+                  </row>
+                  <row>
+                     
+                     <cell>F1<hi rend="super">e</hi> Gute &amp; ZWB</cell>
+                     
+                     <cell>0,63</cell>
+                     
+                     <cell>0,62</cell>
+                     
+                     <cell>
+                        <hi rend="bold">0,67</hi>
+                     </cell>
+                     
+                  </row>
+                  <row>
+                     
+                     <cell>F1 Gute</cell>
+                     
+                     <cell>0,63</cell>
+                     
+                     <cell>0,54</cell>
+                     
+                     <cell>0,61</cell>
+                     
+                  </row>
+                  <row>
+                     
+                     <cell>F1 ZWB</cell>
+                     
+                     <cell>0,48</cell>
+                     
+                     <cell>0,47</cell>
+                     
+                     <cell>0,57</cell>
+                     
+                  </row>
+                  <row>
+                     
+                     <cell>F1 Basis</cell>
+                     
+                     <cell>0,48</cell>
+                     
+                     <cell>0,47</cell>
+                     
+                     <cell>0,53</cell>
+                     
+                  </row>
+                  <row>
+                     
+                     <cell/>
+                     
+                     <cell/>
+                     
+                     <cell/>
+                     
+                     <cell/>
+                     
+                  </row>
+                  <row>
+                     
+                     <cell>EQ<hi rend="super">f</hi> Gute &amp; ZWB</cell>
+                     
+                     <cell>55</cell>
+                     
+                     <cell>57,5</cell>
+                     
+                     <cell>
+                        <hi rend="bold">65</hi>
+                     </cell>
+                     
+                  </row>
+                  <row>
+                     
+                     <cell>EQ Gute</cell>
+                     
+                     <cell>56,67</cell>
+                     
+                     <cell>50</cell>
+                     
+                     <cell>60</cell>
+                     
+                  </row>
+                  <row>
+                     
+                     <cell>EQ ZWB</cell>
+                     
+                     <cell>40</cell>
+                     
+                     <cell>40</cell>
+                     
+                     <cell>50</cell>
+                     
+                  </row>
+                  <row>
+                     
+                     <cell>EQ Basis</cell>
+                     
+                     <cell>40</cell>
+                     
+                     <cell>40</cell>
+                     
+                     <cell>46,67</cell>
+                     
+                  </row>
+                  <row>
+                     
+                     <cell/>
+                     
+                     <cell/>
+                     
+                     <cell/>
+                     
+                     <cell/>
+                     
+                  </row>
+                  <row>
+                     
+                     <cell>FP<hi rend="super">g</hi> Gute &amp; ZWB</cell>
+                     
+                     <cell>
+                        <hi rend="bold">18,29</hi>
+                     </cell>
+                     
+                     <cell>28,55</cell>
+                     
+                     <cell>28,68</cell>
+                     
+                  </row>
+                  <row>
+                     
+                     <cell>FP Gute</cell>
+                     
+                     <cell>24,47</cell>
+                     
+                     <cell>36,58</cell>
+                     
+                     <cell>36,67</cell>
+                     
+                  </row>
+                  <row>
+                     
+                     <cell>FP ZWB</cell>
+                     
+                     <cell>27,98</cell>
+                     
+                     <cell>29,65</cell>
+                     
+                     <cell>26,14</cell>
+                     
+                  </row>
+                  <row>
+                     
+                     <cell>FP Basis</cell>
+                     
+                     <cell>27,98</cell>
+                     
+                     <cell>29,65</cell>
+                     
+                     <cell>27,89</cell>
+                     
+                  </row>
+                  <row>
+                     
+                     <cell/>
+                     
+                     <cell/>
+                     
+                     <cell/>
+                     
+                     <cell/>
+                     
+                  </row>
+                  <row>
+                     
+                     <cell>Diff<hi rend="super">h</hi> ARI</cell>
+                     
+                     <cell>0,18</cell>
+                     
+                     <cell>0,08</cell>
+                     
+                     <cell>0,13</cell>
+                     
+                  </row>
+                  <row>
+                     
+                     <cell>Diff EQ</cell>
+                     
+                     <cell>15,00</cell>
+                     
+                     <cell>17,50</cell>
+                     
+                     <cell>18,33</cell>
+                     
+                  </row>
+                  <row>
+                     
+                     <cell>Diff FP</cell>
+                     
+                     <cell>9,69</cell>
+                     
+                     <cell>1,10</cell>
+                     
+                     <cell>-0,79</cell>
+                     
+                  </row>
+                  <row>
+                     <cell cols="4"><hi rend="super">a</hi> ARI: Adjusted Rand Index<lb/>
+                        <hi rend="super">b</hi> Gute: Mit Gute-Wörter-Liste<lb/>
+                           <hi rend="super">c</hi> ZWB: Mit Z-Wert-Begrenzung auf 1,64<lb/>
+                              <hi rend="super">d</hi> Basis: Ohne Gute-Wörter-Liste und ohne Z-Wert-Begrenzung<lb/>
+                                 <hi rend="super">e</hi> F1: Precision und Recall kombiniert<lb/>
+                                    <hi rend="super">f</hi> EQ: Erkennungsquote in %<lb/>
+                                       <hi rend="super">g</hi> FP: False-Positives-Quote in&#160;% (niedriger ist besser)<lb/>
+                                          <hi rend="super">h</hi> Diff: Differenzen zwischen Werten mit Gute-Wörter-Liste und mit Z-Wert-Begrenzung zum Basiswert
+                     </cell>
+                  </row>
+                  <trailer xml:id="tab010"><ref type="intern" target="#tab10">Tab. 10</ref>: Test D2, ARI-Test mit 2 × 10 Distraktortexten, ABE, BIL, GES.<ref type="graphic" target="#gattungssignal_2022_t10"/></trailer>
+               </table>
+            <p>Wiederum ist, wie ein Blick in die Subgenre-Einzelwerte in der Folgetabelle zeigt,
+               die Erkennung beim Abenteuerroman deutlich besser, das Clustering beim
+               Gesellschaftsroman ist schlechter als eine Zufallsverteilung, es gibt über ein
+               Drittel False-Positives. Damit hängt zusammen, dass das Niveau in der vorausgehenden
+               Tabelle deutlich niedriger ist als bei den Testreihen A und B. Wiederum ist die
+               Z-Wert-Begrenzung für eine Verbesserung bei den False-Positives und die
+               Gute-Wörter-Technik für eine Verbesserung der Erkennungsquote verantwortlich.</p>
+               <table rend="rules" xml:id="gattungssignal_2022_t11">
+                  <row>
+                     
+                     <cell>200 MFWs</cell>
+                     
+                     <cell>ARI<hi rend="super">a</hi></cell>
+                     
+                     <cell>F1<hi rend="super">b</hi></cell>
+                     
+                     <cell>EQ<hi rend="super">c</hi></cell>
+                     
+                     <cell>FP<hi rend="super">d</hi></cell>
+                     
+                  </row>
+                  <row>
+                     
+                     <cell>ABE</cell>
+                     
+                     <cell>0,413</cell>
+                     
+                     <cell>0,73</cell>
+                     
+                     <cell>60</cell>
+                     
+                     <cell>5</cell>
+                     
+                  </row>
+                  <row>
+                     
+                     <cell>BIL</cell>
+                     
+                     <cell>0,06</cell>
+                     
+                     <cell>0,57</cell>
+                     
+                     <cell>50</cell>
+                     
+                     <cell>26,3</cell>
+                     
+                  </row>
+                  <row>
+                     
+                     <cell>GES</cell>
+                     
+                     <cell>-0,005</cell>
+                     
+                     <cell>0,54</cell>
+                     
+                     <cell>50</cell>
+                     
+                     <cell>36,8</cell>
+                     
+                  </row>
+                  <row>
+                     <cell cols="5"><hi rend="super">a</hi> ARI: Adjusted Rand Index<lb/>
+                        <hi rend="super">b</hi> F1: Precision und Recall kombiniert<lb/>
+                        <hi rend="super">c</hi> EQ: Erkennungsquote in %<lb/>
+                              <hi rend="super">d</hi> FP: False-Positives-Quote in&#160;% (niedriger ist besser)
+                     </cell>
+                  </row>
+                  <trailer xml:id="tab011"><ref type="intern" target="#tab11">Tab. 11</ref>: Test D2, ARI-Test mit 2 × 10 Distraktortexten, Einzelwerte, ABE,
+   BIL, GES. <ref type="graphic" target="#gattungssignal_2022_t11"/></trailer>
+</table>
+            </div>
+            <div type="subchapter">
+               <head>D3: ARI-Test mit 2 × 5 Distraktortexten</head>
+            
+            <p>Die gleiche Tendenz auf noch schlechterem Niveau zeigt sich bei der Variante mit nur
+               fünf (statt zehn) ausgelosten Distraktortexten je Nicht-Zielklasse:</p>
+               <table rend="rules" xml:id="gattungssignal_2022_t12">
+                  <row>
+                     <cell>Volltexte</cell>
+                     <cell>200</cell>
+                     <cell>300</cell>
+                     <cell>400</cell>
+                  </row>
+                  <row>
+                     <cell>ARI<hi rend="super">a</hi> Gute<hi rend="super">b</hi> &amp; ZWB<hi rend="super">c</hi></cell>
+                     <cell>0,06</cell>
+                     <cell>0,05</cell>
+                     <cell>0,05</cell>
+                  </row>
+                  <row>
+                     <cell>ARI Gute</cell>
+                     <cell>0,04</cell>
+                     <cell>0,05</cell>
+                     <cell>
+                        <hi rend="bold">0,07</hi>
+                     </cell>
+                  </row>
+                  <row>
+                     <cell>ARI ZWB</cell>
+                     <cell>0,03</cell>
+                     <cell>0,00</cell>
+                     <cell>0,04</cell>
+                  </row>
+                  <row>
+                     <cell>ARI Basis<hi rend="super">d</hi></cell>
+                     <cell>-0,01</cell>
+                     <cell>-0,01</cell>
+                     <cell>0,02</cell>
+                  </row>
+                  <row>
+                     <cell/>
+                     <cell/>
+                     <cell/>
+                     <cell/>
+                  </row>
+                  <row>
+                     <cell>F1<hi rend="super">e</hi> Gute &amp; ZWB</cell>
+                     <cell>0,60</cell>
+                     <cell>0,62</cell>
+                     <cell>0,64</cell>
+                  </row>
+                  <row>
+                     <cell>F1 Gute</cell>
+                     <cell>0,63</cell>
+                     <cell>0,61</cell>
+                     <cell>
+                        <hi rend="bold">0,65</hi>
+                     </cell>
+                  </row>
+                  <row>
+                     <cell>F1 ZWB</cell>
+                     <cell>0,52</cell>
+                     <cell>0,54</cell>
+                     <cell>0,59</cell>
+                  </row>
+                  <row>
+                     <cell>F1 Basis</cell>
+                     <cell>0,54</cell>
+                     <cell>0,55</cell>
+                     <cell>0,58</cell>
+                  </row>
+                  <row>
+                     <cell/>
+                     <cell/>
+                     <cell/>
+                     <cell/>
+                  </row>
+                  <row>
+                     <cell>EQ<hi rend="super">f</hi> Gute &amp; ZWB</cell>
+                     <cell>59,00</cell>
+                     <cell>65,00</cell>
+                     <cell>68,30</cell>
+                  </row>
+                  <row>
+                     <cell>EQ Gute</cell>
+                     <cell>67,67</cell>
+                     <cell>66,00</cell>
+                     <cell>
+                        <hi rend="bold">72,30</hi>
+                     </cell>
+                  </row>
+                  <row>
+                     <cell>EQ ZWB</cell>
+                     <cell>52,70</cell>
+                     <cell>55,00</cell>
+                     <cell>61,30</cell>
+                  </row>
+                  <row>
+                     <cell>EQ Basis</cell>
+                     <cell>55,67</cell>
+                     <cell>56,33</cell>
+                     <cell>60,00</cell>
+                  </row>
+                  <row>
+                     <cell/>
+                     <cell/>
+                     <cell/>
+                     <cell/>
+                  </row>
+                  <row>
+                     <cell>FP<hi rend="super">g</hi> Gute &amp; ZWB</cell>
+                     <cell>
+                        <hi rend="bold">38,70</hi>
+                     </cell>
+                     <cell>46,00</cell>
+                     <cell>46,00</cell>
+                  </row>
+                  <row>
+                     <cell>FP Gute</cell>
+                     <cell>46,33</cell>
+                     <cell>50,67</cell>
+                     <cell>48,70</cell>
+                  </row>
+                  <row>
+                     <cell>FP ZWB</cell>
+                     <cell>49,00</cell>
+                     <cell>48,00</cell>
+                     <cell>46,30</cell>
+                  </row>
+                  <row>
+                     <cell>FP Basis</cell>
+                     <cell>49,33</cell>
+                     <cell>49,33</cell>
+                     <cell>47,67</cell>
+                  </row>
+                  <row>
+                     <cell/>
+                     <cell/>
+                     <cell/>
+                     <cell/>
+                  </row>
+                  <row>
+                     <cell>Diff<hi rend="super">h</hi> ARI</cell>
+                     <cell>0,07</cell>
+                     <cell>0,06</cell>
+                     <cell>0,03</cell>
+                  </row>
+                  <row>
+                     <cell>Diff EQ</cell>
+                     <cell>3,33</cell>
+                     <cell>8,67</cell>
+                     <cell>8,30</cell>
+                  </row>
+                  <row>
+                     <cell>Diff FP</cell>
+                     <cell>10,62</cell>
+                     <cell>3,33</cell>
+                     <cell>1,67</cell>
+                  </row>
+                  <row>
+                     <cell cols="4"><hi rend="super">a</hi> ARI: Adjusted Rand Index<lb/>
+                        <hi rend="super">b</hi> Gute: Mit Gute-Wörter-Liste<lb/>
+                        <hi rend="super">c</hi> ZWB: Mit Z-Wert-Begrenzung auf 1,64<lb/>
+                        <hi rend="super">d</hi> Basis: Ohne Gute-Wörter-Liste und ohne Z-Wert-Begrenzung<lb/>
+                        <hi rend="super">e</hi> F1: Precision und Recall kombiniert<lb/>
+                        <hi rend="super">f</hi> EQ: Erkennungsquote in %<lb/>
+                        <hi rend="super">g</hi> FP: False-Positives-Quote in&#160;% (niedriger ist besser)<lb/>
+                        <hi rend="super">h</hi> Diff: Differenzen zwischen Werten mit Gute-Wörter-Liste und mit Z-Wert-Begrenzung zum Basiswert
+                     </cell>
+                  </row>
+                  <trailer xml:id="tab012"><ref type="intern" target="#tab12">Tab. 12</ref>: Test D3, ARI-Test mit 2 × 5 Distraktortexten, ABE, BIL, GES. <ref type="graphic" target="#gattungssignal_2022_t12"/></trailer>
+               </table>
+            <p>Problematisch an diesem Setting ist die Kombination von zehn Distraktortexten mit
+               neun Zielklassentexten, gegen die im Einzeltest ein Zielklassentext getestet wird.
+               Eine Zufallsverteilung würde eine Erkennungsquote von 31&#160;% ergeben. Die
+               Erkennungsquoten müssten also deutlich höher liegen, um einen guten ARI-Wert zu
+               erzielen. Vor allem ist hier die False-Positives-Rate ausgesprochen schlecht, sie
+               wird auch durch die Z-Wert-Begrenzung nur marginal verbessert. Anders als in den
+               anderen Testreihen sinkt die False-Positives-Rate erst in der Kombination der beiden
+               Optimierungstechniken, allerdings nicht auf ein ordentliches Niveau.</p>
+            </div>
+            <div type="subchapter">
+               <head>D4: ARI-Test mit 2 × 10 Distraktortexten und größerem MFW-Bereich</head>
+            
+            <p>Da die kürzeren Komödien und Tragödien hier unberücksichtigt bleiben, wird es
+               möglich, einen größeren Bereich an MFWs in den Test einzubeziehen. In den übrigen
+               Testreihen werden Listen mit Guten Wörtern verwendet, die mithilfe von 1.200 MFWs
+               ermittelt wurden. Die Anzahl dieser Guten Wörter, deren Level-2-Differenz &gt;0,2
+               beträgt, liegt dort zwischen 495 und 637 Wortformen. Hier wurden nun die guten Wörter
+               auf der Grundlage von 5.000 MFWs berechnet. Die Anzahl dieser Guten Wörter, deren
+               Level-2-Differenz &gt;0,2 beträgt, liegt hier nun bei 2.572 (ABE), 2.405 (BIL) und
+               2.530 (GES) Wortformen. Bei der Evaluation werden nun 500–4.000 MFWs verwendet. </p>
+            <p>Neben den üblichen Tests (in der Folgetabelle von unten nach oben: ›Basis‹: ohne Gute
+               Wörter, ohne Z-Wert-Begrenzung; ›ZWB 1,64‹: nur Z-Wert-Begrenzung, ohne Gute Wörter;
+               ›Gute‹: nur Gute Wörter, ohne Z-Wert-Begrenzung) werden verschiedene Z-Wert-Parameter
+               in Kombination mit den Gute-Wörter-Listen getestet: Bei ›ZWBneg‹ werden positive
+               Z-Werte auf +1,64 und negative Z-Werte auf -0,7 begrenzt, bei ›ZWB 1,0‹, ›ZWB 1,2‹
+               und ›ZWB 1,64‹ werden wie auch sonst die positiven und die negativen Z-Werte auf den
+               Betrag der angegeben Werte begrenzt. </p>
+                        <table rend="rules" xml:id="gattungssignal_2022_t13">
+               <row>
+                  <cell>Volltexte</cell>
+                  <cell>500</cell>
+                  <cell>1000</cell>
+                  <cell>1500</cell>
+                  <cell>2000</cell>
+                  <cell>2500</cell>
+                  <cell>3000</cell>
+                  <cell>3500</cell>
+                  <cell>4000</cell>
+               </row>
+               <row>
+                  <cell>ARI<hi rend="super">a</hi> Gute<hi rend="super">b</hi> &amp; ZWB<hi rend="super">c</hi> 1,64</cell>
+                  <cell>0,19</cell>
+                  <cell>0,19</cell>
+                  <cell>0,25</cell>
+                  <cell>0,09</cell>
+                  <cell>0,09</cell>
+                  <cell>0,27</cell>
+                  <cell>0,3</cell>
+                  <cell>0,22</cell>
+               </row>
+               <row>
+                  <cell>ARI Gute &amp; ZWB 1,2</cell>
+                  <cell>0,17</cell>
+                  <cell>0,17</cell>
+                  <cell>0,23</cell>
+                  <cell>0,17</cell>
+                  <cell>0,16</cell>
+                  <cell>
+                     <hi rend="bold">0,31</hi>
+                  </cell>
+                  <cell>0,28</cell>
+                  <cell>0,28</cell>
+               </row>
+               <row>
+                  <cell>ARI Gute &amp; ZWB 1,0</cell>
+                  <cell>0,14</cell>
+                  <cell>0,18</cell>
+                  <cell>0,19</cell>
+                  <cell>0,21</cell>
+                  <cell>0,16</cell>
+                  <cell>0,29</cell>
+                  <cell>
+                     <hi rend="bold">0,33</hi>
+                  </cell>
+                  <cell>0,3</cell>
+               </row>
+               <row>
+                  <cell>ARI Gute &amp; ZWBneg</cell>
+                  <cell>0,17</cell>
+                  <cell>0,15</cell>
+                  <cell>0,22</cell>
+                  <cell>0,21</cell>
+                  <cell>0,08</cell>
+                  <cell>0,3</cell>
+                  <cell>0,26</cell>
+                  <cell>0,21</cell>
+               </row>
+               <row>
+                  <cell>ARI Gute</cell>
+                  <cell>0,13</cell>
+                  <cell>0,16</cell>
+                  <cell>0,22</cell>
+                  <cell>0,11</cell>
+                  <cell>0,09</cell>
+                  <cell>0,23</cell>
+                  <cell>0,21</cell>
+                  <cell>0,13</cell>
+               </row>
+               <row>
+                  <cell>ARI ZWB 1,64</cell>
+                  <cell>0,13</cell>
+                  <cell>0,2</cell>
+                  <cell>0,2</cell>
+                  <cell>0,18</cell>
+                  <cell>0,11</cell>
+                  <cell>0,11</cell>
+                  <cell>0,1</cell>
+                  <cell>0,11</cell>
+               </row>
+               <row>
+                  <cell>ARI Basis<hi rend="super">d</hi></cell>
+                  <cell>0,08</cell>
+                  <cell>0,11</cell>
+                  <cell>0,14</cell>
+                  <cell>0,14</cell>
+                  <cell>0,11</cell>
+                  <cell>0,08</cell>
+                  <cell>0,09</cell>
+                  <cell>0,08</cell>
+               </row>
+               <row>
+                  <cell/>
+                  <cell/>
+                  <cell/>
+                  <cell/>
+                  <cell/>
+                  <cell/>
+                  <cell/>
+                  <cell/>
+                  <cell/>
+               </row>
+               <row>
+                  <cell>F1<hi rend="super">e</hi> Gute &amp; ZWB 1,64</cell>
+                  <cell>0,68</cell>
+                  <cell>0,66</cell>
+                  <cell>0,68</cell>
+                  <cell>0,65</cell>
+                  <cell>0,66</cell>
+                  <cell>0,73</cell>
+                  <cell>0,72</cell>
+                  <cell>0,66</cell>
+               </row>
+               <row>
+                  <cell>F1 Gute &amp; ZWB 1,2</cell>
+                  <cell>0,67</cell>
+                  <cell>0,69</cell>
+                  <cell>0,71</cell>
+                  <cell>0,68</cell>
+                  <cell>0,70</cell>
+                  <cell>0,74</cell>
+                  <cell>0,73</cell>
+                  <cell>0,70</cell>
+               </row>
+               <row>
+                  <cell>F1 Gute &amp; ZWB 1,0</cell>
+                  <cell>0,66</cell>
+                  <cell>0,71</cell>
+                  <cell>0,69</cell>
+                  <cell>0,70</cell>
+                  <cell>0,68</cell>
+                  <cell>0,73</cell>
+                  <cell>
+                     <hi rend="bold">0,76</hi>
+                  </cell>
+                  <cell>0,74</cell>
+               </row>
+               <row>
+                  <cell>F1 Gute &amp; ZWBneg</cell>
+                  <cell>0,70</cell>
+                  <cell>0,67</cell>
+                  <cell>0,69</cell>
+                  <cell>0,72</cell>
+                  <cell>0,66</cell>
+                  <cell>
+                     <hi rend="bold">0,75</hi>
+                  </cell>
+                  <cell>0,72</cell>
+                  <cell>0,65</cell>
+               </row>
+               <row>
+                  <cell>F1 Gute</cell>
+                  <cell>0,62</cell>
+                  <cell>0,66</cell>
+                  <cell>0,66</cell>
+                  <cell>0,66</cell>
+                  <cell>0,68</cell>
+                  <cell>0,71</cell>
+                  <cell>0,69</cell>
+                  <cell>0,61</cell>
+               </row>
+               <row>
+                  <cell>F1 ZWB 1,64</cell>
+                  <cell>0,57</cell>
+                  <cell>0,66</cell>
+                  <cell>0,66</cell>
+                  <cell>0,66</cell>
+                  <cell>0,57</cell>
+                  <cell>0,59</cell>
+                  <cell>0,56</cell>
+                  <cell>0,59</cell>
+               </row>
+               <row>
+                  <cell>F1 Basis</cell>
+                  <cell>0,51</cell>
+                  <cell>0,60</cell>
+                  <cell>0,63</cell>
+                  <cell>0,63</cell>
+                  <cell>0,59</cell>
+                  <cell>0,53</cell>
+                  <cell>0,56</cell>
+                  <cell>0,53</cell>
+               </row>
+               <row>
+                  <cell/>
+                  <cell/>
+                  <cell/>
+                  <cell/>
+                  <cell/>
+                  <cell/>
+                  <cell/>
+                  <cell/>
+                  <cell/>
+               </row>
+               <row>
+                  <cell>EQ<hi rend="super">f</hi> Gute &amp; ZWB 1,64</cell>
+                  <cell>66,67</cell>
+                  <cell>66,67</cell>
+                  <cell>66,67</cell>
+                  <cell>66,67</cell>
+                  <cell>66,67</cell>
+                  <cell>70</cell>
+                  <cell>66,67</cell>
+                  <cell>60</cell>
+               </row>
+               <row>
+                  <cell>EQ Gute &amp; ZWB 1,2</cell>
+                  <cell>63,33</cell>
+                  <cell>66,67</cell>
+                  <cell>70</cell>
+                  <cell>66,67</cell>
+                  <cell>70</cell>
+                  <cell>70</cell>
+                  <cell>70</cell>
+                  <cell>66,67</cell>
+               </row>
+               <row>
+                  <cell>EQ Gute &amp; ZWB 1,0</cell>
+                  <cell>63,33</cell>
+                  <cell>70</cell>
+                  <cell>66,67</cell>
+                  <cell>66,67</cell>
+                  <cell>66,67</cell>
+                  <cell>70</cell>
+                  <cell>
+                     <hi rend="bold">73,33</hi>
+                  </cell>
+                  <cell>70</cell>
+               </row>
+               <row>
+                  <cell>EQ Gute &amp; ZWBneg</cell>
+                  <cell>70</cell>
+                  <cell>70</cell>
+                  <cell>66,67</cell>
+                  <cell>
+                     <hi rend="bold">73,33</hi>
+                  </cell>
+                  <cell>66,67</cell>
+                  <cell>
+                     <hi rend="bold">73,33</hi>
+                  </cell>
+                  <cell>70</cell>
+                  <cell>60</cell>
+               </row>
+               <row>
+                  <cell>EQ Gute</cell>
+                  <cell>60</cell>
+                  <cell>66,67</cell>
+                  <cell>66,67</cell>
+                  <cell>70</cell>
+                  <cell>73,33</cell>
+                  <cell>70</cell>
+                  <cell>66,67</cell>
+                  <cell>56,67</cell>
+               </row>
+               <row>
+                  <cell>EQ ZWB 1,64</cell>
+                  <cell>50</cell>
+                  <cell>60</cell>
+                  <cell>60</cell>
+                  <cell>60</cell>
+                  <cell>50</cell>
+                  <cell>53,33</cell>
+                  <cell>50</cell>
+                  <cell>53,33</cell>
+               </row>
+               <row>
+                  <cell>EQ Basis</cell>
+                  <cell>43,33</cell>
+                  <cell>53,33</cell>
+                  <cell>56,67</cell>
+                  <cell>56,67</cell>
+                  <cell>53,33</cell>
+                  <cell>46,67</cell>
+                  <cell>50</cell>
+                  <cell>46,67</cell>
+               </row>
+               <row>
+                  <cell/>
+                  <cell/>
+                  <cell/>
+                  <cell/>
+                  <cell/>
+                  <cell/>
+                  <cell/>
+                  <cell/>
+                  <cell/>
+               </row>
+               <row>
+                  <cell>FP<hi rend="super">g</hi> Gute &amp; ZWB 1,64</cell>
+                  <cell>29,74</cell>
+                  <cell>35</cell>
+                  <cell>28,07</cell>
+                  <cell>38,25</cell>
+                  <cell>34,74</cell>
+                  <cell>22,72</cell>
+                  <cell>19,3</cell>
+                  <cell>22,81</cell>
+               </row>
+               <row>
+                  <cell>FP Gute &amp; ZWB 1,2</cell>
+                  <cell>26,14</cell>
+                  <cell>27,89</cell>
+                  <cell>27,98</cell>
+                  <cell>29,65</cell>
+                  <cell>31,32</cell>
+                  <cell>
+                     <hi rend="bold">19,21</hi>
+                  </cell>
+                  <cell>20,96</cell>
+                  <cell>22,81</cell>
+               </row>
+               <row>
+                  <cell>FP Gute &amp; ZWB 1,0</cell>
+                  <cell>27,81</cell>
+                  <cell>27,89</cell>
+                  <cell>27,89</cell>
+                  <cell>24,39</cell>
+                  <cell>29,56</cell>
+                  <cell>20,96</cell>
+                  <cell>
+                     <hi rend="bold">19,21</hi>
+                  </cell>
+                  <cell>
+                     <hi rend="bold">19,21</hi>
+                  </cell>
+               </row>
+               <row>
+                  <cell>FP Gute &amp; ZWBneg</cell>
+                  <cell>31,32</cell>
+                  <cell>38,33</cell>
+                  <cell>26,23</cell>
+                  <cell>31,49</cell>
+                  <cell>34,65</cell>
+                  <cell>22,72</cell>
+                  <cell>24,47</cell>
+                  <cell>24,56</cell>
+               </row>
+               <row>
+                  <cell>FP Gute</cell>
+                  <cell>33,25</cell>
+                  <cell>35</cell>
+                  <cell>35,09</cell>
+                  <cell>41,84</cell>
+                  <cell>41,75</cell>
+                  <cell>26,23</cell>
+                  <cell>26,23</cell>
+                  <cell>27,98</cell>
+               </row>
+               <row>
+                  <cell>FP ZWB 1,64</cell>
+                  <cell>26,14</cell>
+                  <cell>20,96</cell>
+                  <cell>20,96</cell>
+                  <cell>22,72</cell>
+                  <cell>24,47</cell>
+                  <cell>26,23</cell>
+                  <cell>27,98</cell>
+                  <cell>27,98</cell>
+               </row>
+               <row>
+                  <cell>FP Basis</cell>
+                  <cell>27,89</cell>
+                  <cell>24,47</cell>
+                  <cell>22,72</cell>
+                  <cell>24,47</cell>
+                  <cell>26,23</cell>
+                  <cell>29,74</cell>
+                  <cell>29,74</cell>
+                  <cell>29,73</cell>
+               </row>
+                           <row>
+                              <cell cols="9"><hi rend="super">a</hi> ARI: Adjusted Rand Index<lb/>
+                                 <hi rend="super">b</hi> Gute: Mit Gute-Wörter-Liste<lb/>
+                                 <hi rend="super">c</hi> ZWB: Mit Z-Wert-Begrenzung auf 1,64<lb/>
+                                 <hi rend="super">d</hi> Basis: Ohne Gute-Wörter-Liste und ohne Z-Wert-Begrenzung<lb/>
+                                 <hi rend="super">e</hi> F1: Precision und Recall kombiniert<lb/>
+                                 <hi rend="super">f</hi> EQ: Erkennungsquote in %<lb/>
+                                 <hi rend="super">g</hi> FP: False-Positives-Quote in&#160;% (niedriger ist besser)<lb/>
+                              </cell>
+                           </row>
+                           <trailer xml:id="tab013"><ref type="intern" target="#tab13">Tab. 13</ref>: Test D4, ARI-Test mit 2 × 10 Distraktortexten und großem
+                        MFW-Bereich, Volltexte, ABE, BIL, GES.<ref type="graphic" target="#gattungssignal_2022_t13"/></trailer>
+                        </table>
+            <p>Bei den optimalen Parametern (Gute Wörter kombiniert mit Z-Wert-Begrenzung auf 1,0)
+               werden sowohl bei ARI, F1-Score, Erkennungsquote und False-Positives die besten Werte
+               erreicht. Der F1-Score verbessert sich gegenüber dem besten Wert in <ref
+                  type="graphic" target="#gattungssignal_2022_t10">Testreihe D2</ref> (0,67) nun auf 0,76. </p>
+            <p>Für die optimalen Werte wurde noch ein Bag-of-Words-Tests durchgeführt mit
+               Bag-of-Words mit je 20.000 Wortformen und 200 Iterationen je Einzelwert (Rechenzeit:
+               gut eine Woche). Die Werte sind hier jedoch wieder schlechter:</p>
+               <table rend="rules" xml:id="gattungssignal_2022_t14">
+                  <row>
+                     
+                     <cell>BOW 20T</cell>
+                     
+                     <cell>3000</cell>
+                     
+                     <cell>3500</cell>
+                     
+                  </row>
+                  <row>
+                     
+                     <cell>ARI<hi rend="super">a</hi> Gute<hi rend="super">b</hi> &amp; ZWB<hi rend="super">c</hi> 1,0</cell>
+                     
+                     <cell>0,14</cell>
+                     
+                     <cell>0,17</cell>
+                     
+                  </row>
+                  <row>
+                     
+                     <cell>F1<hi rend="super">d</hi> Gute &amp; ZWB 1,0</cell>
+                     
+                     <cell>0,66</cell>
+                     
+                     <cell>0,67</cell>
+                     
+                  </row>
+                  <row>
+                     
+                     <cell>EQ<hi rend="super">e</hi> Gute &amp; ZWB 1,0</cell>
+                     
+                     <cell>64,7</cell>
+                     
+                     <cell>
+                        <hi rend="bold">64,2</hi>
+                     </cell>
+                     
+                  </row>
+                  <row>
+                     
+                     <cell>FP<hi rend="super">f</hi> Gute &amp; ZWB 1,0</cell>
+                     
+                     <cell>31,01</cell>
+                     
+                     <cell>27,23</cell>
+                     
+                  </row>
+                  <row>
+                     <cell cols="3"><hi rend="super">a</hi> ARI: Adjusted Rand Index<lb/>
+                        <hi rend="super">b</hi> Gute: Mit Gute-Wörter-Liste<lb/>
+                           <hi rend="super">c</hi> ZWB: Mit Z-Wert-Begrenzung auf 1,64<lb/>
+                              <hi rend="super">d</hi> F1: Precision und Recall kombiniert<lb/>
+                                 <hi rend="super">e</hi> EQ: Erkennungsquote in %<lb/>
+                                    <hi rend="super">f</hi> FP: False-Positives-Quote in&#160;% (niedriger ist besser)
+                     </cell>
+                  </row>
+                  <trailer xml:id="tab014"><ref type="intern" target="#tab14">Tab. 14</ref>: Test D5, ARI-Test mit 2 × 10 Distraktortexten und großem
+   MFW-Bereich, Bag-of-Words (20.000 Wortformen), ABE, BIL, GES.<ref type="graphic" target="#gattungssignal_2022_t14"/></trailer>
+               </table>
+            </div>
+            </div>
+            <div type="chapter">
+               <head>6. Fazit</head>
+            
+            <p>Die Verwendung der Guten Wörter führt zu einer Verbesserung der Erkennungsquoten, die
+               Z-Wert-Begrenzung führt zu einer Verbesserung der False-Positives-Quote. In
+               Kombination führen beide Techniken zu einer Verbesserung der Erkennungsquoten, die
+               nicht auf Kosten einer Verschlechterung der False-Positives-Quote erfolgt&#160;– dies ist
+               auch an den verbesserten ARI-Werten ablesbar. Insgesamt bleibt die Gattungserkennung
+               ein schwieriges Geschäft. In Testreihe B wurden bei der Unterscheidung von
+               Abenteuerroman, Bildungsroman und Komödie ordentliche Ergebnisse und zumindest ein
+               guter F1-Wert &gt;0,9 erzielt. Der Test A2 mit allen fünf Textsorten bringt bei 200
+               MFWs mäßige Erfolge mit F1: 0,68, einer Erkennungsquote von 58&#160;% bei immerhin nur
+               12,4&#160;% False-Positives hervor (etwas verlagert in Test A3: F1: 0,77, Erkennungsquote:
+               76%, False-Positives: 22,1%).</p>
+            <p>In dieser Studie sollte geprüft werden, ob das Gute-Wörter-Verfahren zu einer
+               Verbesserung der Genre-Erkennung beitragen kann. Dazu lässt sich ein positiver Befund
+               festhalten. Wenn man fragt, wie gut die Erkennungsleistung dieser Verfahren bei der
+               Textsortenklassifikation insgesamt ist, ist zu bedenken, dass mit 50 Texten nur ein
+               relativ schmales Korpus evaluiert werden konnte, da Autorduplikate und mehrfache
+               Textsortenlabels vermieden wurden. </p>
+            <p>Die gewählten Bildungs- und Gesellschaftsromane sowie Tragödien und Komödien auf
+               digitalem Weg zu unterscheiden, bleibt eine anspruchsvolle Herausforderung. Zu
+               überlegen wäre, ob die schlechten Werte beim Gesellschaftsroman damit zusammenhängen
+               könnten, dass gesellschaftliche Zustände auch bei den anderen Textsorten eine
+               wichtige Rolle spielen. Die Unterscheidung des Abenteuerromans von Komödie und
+               Tragödie und die Unterscheidung der Komödie von Abenteuer- und Bildungsroman gelingt
+               in diesem Korpus immerhin fehlerfrei (ARI=1). </p></div>
+            <div type="chapter">
+               <head>Anhang: Gute-Wörter-Listen</head>
+            <table rend="rules" xml:id="gattungssignal_2022_t15">
+               <row>
+                    
+                  <cell>ABE</cell>
+                    
+                  <cell>BIL</cell>
+                    
+                  <cell>GES</cell>
+                    
+                  <cell>KOM</cell>
+                    
+                  <cell>TRA</cell>
+                
+               </row>
+               <row>
+                    
+                  <cell>
+                     <list type="unordered">
+                     <item>gang</item>
+                     <item>gilt</item>
+                     <item>herzens</item>
+                     <item>schienen</item>
+                     <item>not</item>
+                     <item>kampf</item>
+                     <item>schlagen</item>
+                     <item>unterbrach</item>
+                     <item>übrigen</item>
+                     <item>schön</item>
+                     <item>o</item>
+                     <item>schwieg</item>
+                     <item>gehn</item>
+                     <item>bisher</item>
+                     <item>ohren</item>
+                     <item>zwischen</item>
+                     <item>geliebten</item>
+                     <item>zukunft</item>
+                     <item>folgte</item>
+                     <item>einsam</item>
+                     <item>geht</item>
+                     <item>name</item>
+                     <item>was</item>
+                     <item>bitte</item>
+                     <item>sehn</item>
+                     <item>ach</item>
+                     <item>ha</item>
+                     <item>ewigen</item>
+                     <item>ward</item>
+                     <item>seltsam</item>
+                     <item>bin</item>
+                     <item>nimmer</item>
+                     <item>dessen</item>
+                     <item>gegen</item>
+                     <item>hinzu</item>
+                     <item>fern</item>
+                     <item>liebe</item>
+                     <item>nun</item>
+                     <item>mein</item>
+                     <item>herz</item>
+                     <item>doch</item>
+                     <item>einen</item>
+                     <item>dank</item>
+                     <item>feind</item>
+                     <item>weh</item>
+                     <item>wars</item>
+                     <item>lust</item>
+                     <item>verzeihen</item>
+                     <item>nimmt</item>
+                     <item>zufall</item>
+                     <item>gehalten</item>
+                     <item>getroffen</item>
+                     <item>tritt</item>
+                     <item>geh</item>
+                     <item>indes</item>
+                     <item>glücklich</item>
+                     <item>eignen</item>
+                     <item>denk</item>
+                     <item>hört</item>
+                     <item>fürstin</item>
+                     <item>macht</item>
+                     <item>glück</item>
+                     <item>ehre</item>
+                     <item>schrieb</item>
+                     <item>tu</item>
+                     <item>spielen</item>
+                     <item>sie</item>
+                     <item>treiben</item>
+                     <item>fällt</item>
+                     <item>sollst</item>
+                     <item>pflicht</item>
+                     <item>siehst</item>
+                     <item>bitten</item>
+                     <item>eh</item>
+                     <item>hörte</item>
+                     <item>daraus</item>
+                     <item>tod</item>
+                     <item>bekannt</item>
+                     <item>hieß</item>
+                     <item>offen</item>
+                     <item>ichs</item>
+                     <item>wußte</item>
+                     <item>tode</item>
+                     <item>allem</item>
+                     <item>herr</item>
+                     <item>sag</item>
+                     <item>muß</item>
+                     <item>zu</item>
+                     <item>lieb</item>
+                     <item>all</item>
+                     <item>ist</item>
+                     <item>lieben</item>
+                     <item>halt</item>
+                     <item>oft</item>
+                     <item>alles</item>
+                     <item>nimm</item>
+                     <item>hatte</item>
+                     <item>berlin</item>
+                     <item>bleibt</item>
+                     <item>sagt</item>
+                     </list>
+                  </cell>
+                    
+                  <cell>
+                     <list type="unordered"><item>sicherheit</item>
+                     <item>knaben</item>
+                     <item>sorgen</item>
+                     <item>erzählt</item>
+                     <item>knabe</item>
+                     <item>diesmal</item>
+                     <item>erklärte</item>
+                     <item>saßen</item>
+                     <item>doch</item>
+                     <item>seien</item>
+                     <item>weh</item>
+                     <item>soll</item>
+                     <item>war</item>
+                     <item>hier</item>
+                     <item>kennt</item>
+                     <item>nein</item>
+                     <item>ha</item>
+                     <item>in</item>
+                     <item>holen</item>
+                     <item>blieben</item>
+                     <item>ecke</item>
+                     <item>was</item>
+                     <item>halt</item>
+                     <item>stets</item>
+                     <item>legen</item>
+                     <item>sage</item>
+                     <item>wollen</item>
+                     <item>wußte</item>
+                     <item>mirs</item>
+                     <item>ah</item>
+                     <item>geh</item>
+                     <item>gefangen</item>
+                     <item>kommen</item>
+                     <item>geht</item>
+                     <item>wort</item>
+                     <item>bin</item>
+                     <item>frieden</item>
+                     <item>sieh</item>
+                     <item>ja</item>
+                     <item>kenne</item>
+                     <item>will</item>
+                     <item>kommt</item>
+                     <item>gott</item>
+                     <item>fall</item>
+                     <item>konnte</item>
+                     <item>streckte</item>
+                     <item>hast</item>
+                     <item>bitte</item>
+                     <item>oh</item>
+                     <item>ort</item>
+                     <item>müssen</item>
+                     <item>kampf</item>
+                     <item>waffen</item>
+                     <item>rasch</item>
+                     <item>allerdings</item>
+                     <item>laßt</item>
+                     <item>ei</item>
+                     <item>kapitel</item>
+                     <item>waren</item>
+                     <item>unmöglich</item>
+                     <item>sollen</item>
+                     <item>durch</item>
+                     <item>sies</item>
+                     <item>fällt</item>
+                     <item>herr</item>
+                     <item>hieß</item>
+                     <item>verließ</item>
+                     <item>erzählen</item>
+                     <item>giebt</item>
+                     <item>heftig</item>
+                     <item>lassen</item>
+                     <item>lieb</item>
+                     <item>hm</item>
+                     <item>gegangen</item>
+                     <item>wahr</item>
+                     <item>ab</item>
+                     <item>tag</item>
+                     <item>komm</item>
+                     <item>drückte</item>
+                     <item>also</item>
+                     <item>hierher</item>
+                     <item>über</item>
+                     <item>hören</item>
+                     <item>denkt</item>
+                     <item>euer</item>
+                     <item>ohr</item>
+                     <item>tränen</item>
+                     <item>besser</item>
+                     <item>arm</item>
+                     <item>ersten</item>
+                     <item>bringt</item>
+                     <item>dienst</item>
+                     <item>bringen</item>
+                     <item>aus</item>
+                     <item>not</item>
+                     <item>sollst</item>
+                     <item>berlin</item>
+                     <item>frei</item>
+                     <item>bord</item>
+                     <item>meinst</item></list>
+                  </cell>
+                    
+                  <cell>
+                     <list type="unordered"><item>wißt</item>
+                     <item>zieht</item>
+                     <item>strom</item>
+                     <item>jenem</item>
+                     <item>nase</item>
+                     <item>obgleich</item>
+                     <item>höher</item>
+                     <item>zorn</item>
+                     <item>stimmen</item>
+                     <item>mich</item>
+                     <item>fern</item>
+                     <item>ich</item>
+                     <item>mir</item>
+                     <item>weiber</item>
+                     <item>erklärte</item>
+                     <item>körper</item>
+                     <item>ha</item>
+                     <item>o</item>
+                     <item>hatte</item>
+                     <item>ziel</item>
+                     <item>aufmerksamkeit</item>
+                     <item>schlagen</item>
+                     <item>wars</item>
+                     <item>meinem</item>
+                     <item>setzt</item>
+                     <item>meines</item>
+                     <item>waffen</item>
+                     <item>meiner</item>
+                     <item>meinen</item>
+                     <item>denken</item>
+                     <item>als</item>
+                     <item>offen</item>
+                     <item>hört</item>
+                     <item>hölle</item>
+                     <item>seid</item>
+                     <item>eure</item>
+                     <item>meine</item>
+                     <item>allerlei</item>
+                     <item>machte</item>
+                     <item>ruf</item>
+                     <item>euren</item>
+                     <item>soll</item>
+                     <item>seufzte</item>
+                     <item>eurer</item>
+                     <item>macht</item>
+                     <item>setzte</item>
+                     <item>sich</item>
+                     <item>tische</item>
+                     <item>mein</item>
+                     <item>hielten</item>
+                     <item>gestalten</item>
+                     <item>bin</item>
+                     <item>uns</item>
+                     <item>deine</item>
+                     <item>niemals</item>
+                     <item>hilfe</item>
+                     <item>deinen</item>
+                     <item>sagt</item>
+                     <item>steht</item>
+                     <item>deines</item>
+                     <item>euer</item>
+                     <item>abschied</item>
+                     <item>indes</item>
+                     <item>manchmal</item>
+                     <item>wahrhaftig</item>
+                     <item>lebt</item>
+                     <item>befehl</item>
+                     <item>still</item>
+                     <item>tritt</item>
+                     <item>deren</item>
+                     <item>tod</item>
+                     <item>denkt</item>
+                     <item>gefahr</item>
+                     <item>führt</item>
+                     <item>wollt</item>
+                     <item>euch</item>
+                     <item>lager</item>
+                     <item>männer</item>
+                     <item>setzen</item>
+                     <item>laut</item>
+                     <item>gebracht</item>
+                     <item>sah</item>
+                     <item>schöner</item>
+                     <item>lebe</item>
+                     <item>vertrauen</item>
+                     <item>plan</item>
+                     <item>gott</item>
+                     <item>hunde</item>
+                     <item>mittel</item>
+                     <item>kommt</item>
+                     <item>spricht</item>
+                     <item>fällt</item>
+                     <item>verlassen</item>
+                     <item>ruhig</item>
+                     <item>braut</item>
+                     <item>sollst</item>
+                     <item>fragte</item>
+                     <item>will</item>
+                     <item>schmerz</item>
+                     <item>halten</item></list>
+                  </cell>
+                    
+                  <cell>
+                     <list type="unordered"><item>stieß</item>
+                     <item>tages</item>
+                     <item>schlug</item>
+                     <item>kannte</item>
+                     <item>riß</item>
+                     <item>hing</item>
+                     <item>lag</item>
+                     <item>ergriff</item>
+                     <item>blieben</item>
+                     <item>erschien</item>
+                     <item>flog</item>
+                     <item>standen</item>
+                     <item>hielt</item>
+                     <item>fuhr</item>
+                     <item>stieg</item>
+                     <item>empor</item>
+                     <item>war</item>
+                     <item>und</item>
+                     <item>trat</item>
+                     <item>fiel</item>
+                     <item>weiten</item>
+                     <item>wilden</item>
+                     <item>hatte</item>
+                     <item>wurde</item>
+                     <item>wolken</item>
+                     <item>öffnete</item>
+                     <item>reichte</item>
+                     <item>wenigen</item>
+                     <item>zwischen</item>
+                     <item>dessen</item>
+                     <item>ging</item>
+                     <item>mochte</item>
+                     <item>ist</item>
+                     <item>suchte</item>
+                     <item>lachte</item>
+                     <item>folgte</item>
+                     <item>schien</item>
+                     <item>hob</item>
+                     <item>mannes</item>
+                     <item>stand</item>
+                     <item>ich</item>
+                     <item>wußte</item>
+                     <item>neben</item>
+                     <item>schob</item>
+                     <item>weile</item>
+                     <item>tiefer</item>
+                     <item>stellte</item>
+                     <item>wand</item>
+                     <item>konnte</item>
+                     <item>hörte</item>
+                     <item>blickte</item>
+                     <item>griff</item>
+                     <item>des</item>
+                     <item>sprang</item>
+                     <item>erhob</item>
+                     <item>gespräch</item>
+                     <item>lächeln</item>
+                     <item>mußten</item>
+                     <item>schritte</item>
+                     <item>meer</item>
+                     <item>warf</item>
+                     <item>ließ</item>
+                     <item>las</item>
+                     <item>verließ</item>
+                     <item>sah</item>
+                     <item>wandte</item>
+                     <item>sies</item>
+                     <item>faßte</item>
+                     <item>regen</item>
+                     <item>kaum</item>
+                     <item>richtung</item>
+                     <item>erkannte</item>
+                     <item>fühlte</item>
+                     <item>durfte</item>
+                     <item>hat</item>
+                     <item>erzählte</item>
+                     <item>unterbrach</item>
+                     <item>obwohl</item>
+                     <item>gehalten</item>
+                     <item>zug</item>
+                     <item>gab</item>
+                     <item>dasselbe</item>
+                     <item>fragte</item>
+                     <item>schatten</item>
+                     <item>rief</item>
+                     <item>zog</item>
+                     <item>langsam</item>
+                     <item>blieb</item>
+                     <item>drückte</item>
+                     <item>gegenüber</item>
+                     <item>schüttelte</item>
+                     <item>einzelne</item>
+                     <item>traf</item>
+                     <item>stimme</item>
+                     <item>tief</item>
+                     <item>sagte</item>
+                     <item>lagen</item>
+                     <item>hatten</item>
+                     <item>antlitz</item>
+                     <item>trieb</item></list>
+                  </cell>
+                    
+                  <cell>
+                     <list type="unordered"><item>mußte</item>
+                     <item>mochte</item>
+                     <item>fuhr</item>
+                     <item>blieben</item>
+                     <item>einigen</item>
+                     <item>hatte</item>
+                     <item>weder</item>
+                     <item>öffnete</item>
+                     <item>hatten</item>
+                     <item>demselben</item>
+                     <item>waren</item>
+                     <item>erzählen</item>
+                     <item>standen</item>
+                     <item>machte</item>
+                     <item>war</item>
+                     <item>wurde</item>
+                     <item>ewig</item>
+                     <item>frieden</item>
+                     <item>unsere</item>
+                     <item>lächelte</item>
+                     <item>sagte</item>
+                     <item>führte</item>
+                     <item>vielmehr</item>
+                     <item>setzte</item>
+                     <item>zeigte</item>
+                     <item>schwere</item>
+                     <item>sieh</item>
+                     <item>wußte</item>
+                     <item>konnte</item>
+                     <item>konnten</item>
+                     <item>schienen</item>
+                     <item>blieb</item>
+                     <item>während</item>
+                     <item>fragte</item>
+                     <item>heraus</item>
+                     <item>schob</item>
+                     <item>gerade</item>
+                     <item>hinzu</item>
+                     <item>kannte</item>
+                     <item>verschwunden</item>
+                     <item>davon</item>
+                     <item>ziemlich</item>
+                     <item>mußten</item>
+                     <item>anderer</item>
+                     <item>erzählte</item>
+                     <item>wandte</item>
+                     <item>mehrere</item>
+                     <item>erkannte</item>
+                     <item>desselben</item>
+                     <item>unterbrach</item>
+                     <item>sprang</item>
+                     <item>begann</item>
+                     <item>ohne</item>
+                     <item>dabei</item>
+                     <item>schüttelte</item>
+                     <item>drückte</item>
+                     <item>erklärte</item>
+                     <item>beiden</item>
+                     <item>endlich</item>
+                     <item>hundert</item>
+                     <item>nachher</item>
+                     <item>wurden</item>
+                     <item>folgte</item>
+                     <item>fremde</item>
+                     <item>stand</item>
+                     <item>suchte</item>
+                     <item>weniger</item>
+                     <item>dagegen</item>
+                     <item>steht</item>
+                     <item>blickte</item>
+                     <item>bauern</item>
+                     <item>ließen</item>
+                     <item>sprache</item>
+                     <item>worden</item>
+                     <item>doktor</item>
+                     <item>flüsterte</item>
+                     <item>vier</item>
+                     <item>stube</item>
+                     <item>darüber</item>
+                     <item>rief</item>
+                     <item>dorf</item>
+                     <item>erwiderte</item>
+                     <item>stieß</item>
+                     <item>einige</item>
+                     <item>sondern</item>
+                     <item>lachte</item>
+                     <item>richtung</item>
+                     <item>andere</item>
+                     <item>faßte</item>
+                     <item>beinahe</item>
+                     <item>daher</item>
+                     <item>menge</item>
+                     <item>denen</item>
+                     <item>hause</item>
+                     <item>legte</item>
+                     <item>obwohl</item>
+                     <item>einzelne</item>
+                     <item>drei</item>
+                     <item>deren</item>
+                     <item>nämlich</item></list>
+                  </cell>
+                
+               </row>
+               <trailer xml:id="tab015"><ref type="intern" target="#tab15">Tab. 15</ref>: Auszug aus den textsortenspezifischen Gute-Wörter-Listen: Jeweils
+   100 Wortformen mit den höchsten Level-2-Differenzen.<ref type="graphic" target="#gattungssignal_2022_t15gattungssignal_2022_t15"/></trailer>
+            </table>
+            <table rend="rules" xml:id="gattungssignal_2022_t16">
+               <row>
+                  <cell>ABE-BIL</cell>
+                  <cell>51</cell>
+               </row>
+               <row>
+                  <cell>ABE-GES</cell>
+                  <cell>43</cell>
+               </row>
+               <row>
+                  <cell>ABE-KOM</cell>
+                  <cell>37</cell>
+               </row>
+               <row>
+                  <cell>ABE-TRA</cell>
+                  <cell>27</cell>
+               </row>
+               <row>
+                  <cell>BIL-GES</cell>
+                  <cell>42</cell>
+               </row>
+               <row>
+                  <cell>BIL-KOM</cell>
+                  <cell>32</cell>
+               </row>
+               <row>
+                  <cell>BIL-TRA</cell>
+                  <cell>27</cell>
+               </row>
+               <row>
+                  <cell>GES-KOM</cell>
+                  <cell>34</cell>
+               </row>
+               <row>
+                  <cell>GES-TRA</cell>
+                  <cell>30</cell>
+               </row>
+               <row>
+                  <cell>KOM-TRA</cell>
+                  <cell>85</cell>
+               </row>
+               <trailer xml:id="tab016"><ref type="intern" target="#tab16">Tab. 16</ref>: Duplikate in den Listen der Guten Wörter.<ref type="graphic" target="#gattungssignal_2022_t16"/></trailer>
+            </table>
+            <p>Das Verfahren, dass die Guten Wörter für eine Textsorte in Relation zu den vier
+               anderen Textsorten auf Basis der mehrfach gemittelten Level-2-Differenzen gebildet
+               wurden, bringt es mit sich, dass Wortformen auch dann in eine Gute-Wörter-Liste
+               gelangen können, wenn die Unterscheidungsleistung zu zwei anderen Textsorten nur
+               mäßig, die Unterscheidungsleistung zu zwei nochmals anderen Textsorten jedoch hoch
+               ist. Dadurch ist es möglich, dass einige Wortformen in mehreren gattungsspezifischen
+               Listen auftreten. Die hohe Zahl von 85 Duplikaten bei Komödien und Tragödien ist
+               überraschend; womöglich sind hier viele Wortformen eingegangen, die auf den
+               Unterschieden zwischen Drama und Roman beruhen. Damit korrespondieren könnte auch,
+               dass die Guten Wörter nur mäßig dazu beitragen, die F1-Scores bei der Unterscheidung
+               von Komödie und Tragödie zu verbessern, während die Unterscheidung von Drama und
+               Abenteuerroman fehlerfrei gelingt (vgl. <ref type="intern" target="#hd13"
+                  >Test C</ref>).</p>
+            <p>Wörter, die man in semantischer Hinsicht vielleicht auch intuitiv mit der Textsorte
+               in Verbringen wollte, sind in den Gute-Wörter-Listen selten&#160;– die meisten Wortformen
+               findet man auch sonst in längeren MfW-Listen. Wenn man gezielt sucht, könnten etwa
+               ›fern‹ oder ›Zufall‹ typisch für ein Abenteuer-Sujet sein, ›erklärte‹ für den
+               Bildungsroman (wobei diese Wortform auch bei Gesellschaftsroman und Tragödie
+               vorkommt), ›schwere‹ oder ›verschwunden‹ würden in Tragödien nicht überraschen.
+               Allerdings wäre es keine geringe Herausforderung, Kriterien für eine solche Intuition
+               intersubjektiv nachvollziehbar zu begründen.</p>
+            <p>Verben stehen recht erwartbar meist in der 3. Person Singular Präteritum, in der
+               Abenteuerroman-Liste sind jedoch relativ viele Verben in der 2. Person Singular
+               Präsens enthalten&#160;– womöglich ein Indikator für einen erhöhten Anteil an direkter
+               Figurenrede. Dass ›Berlin‹ in den Listen steht, könnte damit korrespondieren, dass
+               nur zehn Texte je Textsorte für die Berechnung der Listen verwendet wurden; bei einem
+               größeren Korpus würden solche vermutlich textspezifischen Wörter nicht in die Listen
+               eingehen.</p>
+            </div>
+         </div>
+            <div type="bibliography">
+               <head>Bibliografische Angaben</head>
+            <listBibl>
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+            </bibl>
+                        <bibl xml:id="büttner_proisl_stilometrie_2016">Andreas Büttner / Thomas Proisl: Stilometrie interdisziplinär: Merkmalsselektion
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+               Leipzig, 07.–12.03.2016) Duisburg 2016, S. 70–74. DOI: <ref
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+               <bibl xml:id="Büttner_et_al_delta_2017">Andreas Büttner / Friedrich Michael Dimpel / Stefan Evert / Fotis
+                  Jannidis / Steffen Pielström / Thomas Proisl / Isabella Reger / Christof
+                  Schöch / Thorsten Vitt: „Delta“ in der stilometrischen Autorschaftsattribution. In:
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+               </bibl>
+               <bibl xml:id="tello_gattungserkennung_2019">José Calvo Tello: Gattungserkennung über 500 Jahre. In: DHd 2019 Digital
+               Humanities: multimedial &amp; multimodal. Konferenzabstracts. Hg. von Patrick Sahle.
+               (DHd: 6, Frankfurt am Main u.&#160;a., 25.–29.03.2019) Frankfurt/Main 2019, S. 292–294.
+               DOI: <ref target="https://doi.org/10.5281/zenodo.2600812"
+                  >10.5281/zenodo.2600812</ref>
+            </bibl>
+               <bibl xml:id="dimpel_computerphilologe_2015">Friedrich Michael Dimpel: Der Computerphilologe als Interpret&#160;– ein Teilzeit-Empiriker? In: Literatur interpretieren. Interdisziplinäre Beiträge zur Theorie und Praxis. Hg. von Jan Borkowski&#160;/ Stefan Descher&#160;/ Felicitas Ferder&#160;/ Philipp Heine. Münster 2015, S.&#160;339–359. DOI: 10.30965/9783957438973_018</bibl>
+            <bibl xml:id="dimpel_töpfchen_2018a">Friedrich Michael Dimpel (2018a): Die guten ins Töpfchen: Zur Anwendbarkeit von
+               Burrows’ Delta bei kurzen mittelhochdeutschen Texten nebst eines Attributionstests zu
+               Konrads ‚Halber Birne‘. In: DHd 2018. Kritik der digitalen Vernunft.
+               Konferenzabstracts. Hg. von Georg Vogeler. (DHd: 5, Köln, 26.02.–02.03.2018) Köln
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+                  >10.5281/zenodo.3684897</ref>
+            </bibl>
+            <bibl xml:id="dimpel_delta_2018b">Friedrich Michael Dimpel (2018b): Ein Delta-Rätsel: Nicht-normalisierte
+               mittelhochdeutsche Texte, Z-Wert-Begrenzung und ein Normalisierungswörterbuch. Oder:
+               Auf welche Wörter kommt es bei Delta an? Göttingen 2018. (=&#160;Dariah-DE Working Papers,
+               25) URN: <ref target="http://nbn-resolving.de/urn:nbn:de:gbv:7-dariah-2017-5-1"
+                  >urn:nbn:de:gbv:7-dariah-2017-5-1</ref>
+            </bibl>
+               <bibl xml:id="dimpel_et_al_streit_2019">Friedrich Michael Dimpel / Daniel Schlager / Katharina Zeppezauer-Wachauer: Der
+                  Streit um die Birne. Autorschafts-Attributionstest mit Burrows’ Delta und dessen
+                  Optimierung für Kurztexte am Beispiel der ‚Halben Birne‘ des Konrad von Würzburg. In:
+                  Digitale Mediävistik. Hg. von Roman Bleier / Franz Fischer / Torsten
+                  Hiltmann / Gabriel Viehhauser / Georg Vogeler. Berlin u.&#160;a. 2019, S. 71–90. <ptr type="gbv" cRef="167073062X"/></bibl>
+               <bibl xml:id="dimpel_proisl_delta_2019">Friedrich Michael Dimpel / Thomas Proisl: Gute Wörter für Delta: Verbesserung der
+                  Autorschaftsattribution durch autorspezifische distinktive Wörter. In: DHd 2019
+                  Digital Humanities: multimedial &amp; multimodal. Konferenzabstracts. Hg. von Patrick
+                  Sahle. (DHd: 6, Frankfurt am Main u.&#160;a., 25.–29.03.2019) Frankfurt/Main 2019,
+                  S. 296–299. DOI: <ref target="https://doi.org/10.5281/zenodo.2600812"
+                     >10.5281/zenodo.2600812</ref>
+               </bibl>
+            <bibl xml:id="eder_rybicki_deeper_2011">Maciej Eder / Jan Rybicki: Deeper Delta Across Genres and Languages: Do We Really
+               Need the Most Frequent Words? In: Literary and Linguistic Computing 26 (2011), H.&#160;3,
+               S.&#160;315–321. DOI: <ref target="https://doi.org/10.1093/llc/fqr031"
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+                  Schöcht&#160;/ Steffen Pielströmt&#160;/ Thorsten Vitt&#160;/ Isabella Regert&#160;/ Andreas Büttner&#160;/ Thomas Proisl: Burrows Delta verstehen. In: Modellierung, Vernetzung,
+               Visualisierung. Die Digital Humanities als fächerübergreifendes Forschungsparadigma.
+               DHd 2016. Konferenzabstracts. Hg. von Elisabeth Burr. 2. überarbeitete und erweiterte
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+            <bibl xml:id="fuchs_hybride_1997">Stephan Fuchs: Hybride Helden: Gwigalois und Willehalm. Beiträge zum Heldenbild
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+               <bibl xml:id="gittel_koeppe_distance_2022">Benjamin Gittel&#160;/ Tilmann Köppe: On the Distance Between Traditional and DH-Based Genre Theory. In: Digitale Verfahren in der Literaturwissenschaft. Hg. von Jan Horstmann&#160;/ Frank Fischer. Münster 2022. (=&#160;Sonderausgabe Textpraxis. Digitales Journal für Philologie, 6). DOI: 10.17879/64059431694</bibl>
+               <bibl xml:id="hettinger_et_al_genre_2015">Lena Hettinger / Martin Becker / Isabella Reger / Fotis Jannidis / Andreas Hotho:
+                  Genre classification on German novels. In: Database and expert systems applications.
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+                  Toumani&#160;/ Roland Wagner&#160;/ Hendrik Decker. (DEXA: 26, Valencia, 01.–04.09.2015). Cham
+                  u.&#160;a. 2015, S. 249–253. DOI: <ref target="https://doi.org/10.1109/DEXA.2015.62"
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+               Humanities 2016. Conference Abstracts. (DH 2016, Krakau, 11.-16.07.2016) Krakau 2016.
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+            </div>
+         <div type="abbildungsnachweis">
+            <head>Tabellenverzeichnis</head>
+            <desc type="table" xml:id="tab1"><ref target="#tab001">Tab. 1</ref>: Test A1, Erkennungsquotentest mit fünf Vergleichstexten der Zielklasse, Test A1, ABE, BIL, GES, KOM, TRA. Beim Bag-of-Words-Test mit 10.000 MFWs werden die Texte 200 verschiedenen Bag-Sets pro Gattung zugelost und Mittelwerte gebildet.<ref type="graphic" target="#gattungssignal_2022_t1"/></desc>
+            <desc type="table" xml:id="tab2"><ref target="#tab002">Tab. 2</ref>: Test A2, ARI-Test mit 4 × 10 Distraktortexten, ABE, BIL, GES, KOM, TRA.<ref type="graphic" target="#gattungssignal_2022_t2"/></desc>
+            <desc type="table" xml:id="tab3"><ref target="#tab003">Tab. 3</ref>: Test A2, ARI-Test mit 4 × 10 Distraktortexten, Einzelwerte, ABE,
+               BIL, GES, KOM, TRA.<ref type="graphic" target="#gattungssignal_2022_t3"/></desc>
+            <desc type="table" xml:id="tab4"><ref target="#tab004">Tab. 4</ref>: Test A2, ARI-Test mit 4 × 10 Distraktortexten, Bag-of-Words, ABE,
+               BIL, GES, KOM, TRA.<ref type="graphic" target="#gattungssignal_2022_t4"/></desc>
+            <desc type="table" xml:id="tab5"><ref target="#tab005">Tab. 5</ref>: Test A3, ARI-Test mit 4 × 5 Distraktortexten, ABE, BIL, GES, KOM,
+               TRA.<ref type="graphic" target="#gattungssignal_2022_t5"/></desc>
+            <desc type="table" xml:id="tab6"><ref target="#tab006">Tab. 6</ref>: Test B, ARI-Test mit 2 × 10 Distraktortexten, ABE, BIL, KOM.<ref type="graphic" target="#gattungssignal_2022_t6"/></desc>
+            <desc type="table" xml:id="tab7"><ref target="#tab007">Tab. 7</ref>: Test C, ARI-Test mit 2 × 10 Distraktortexten, ABE, KOM, TRA.<ref type="graphic" target="#gattungssignal_2022_t7"/></desc>
+            <desc type="table" xml:id="tab8"><ref target="#tab008">Tab. 8</ref>: Test C, ARI-Test mit 2 × 10 Distraktortexten, Einzelwerte, ABE,
+               KOM, TRA (Volltexte, mit Guten Wörtern und Z-Wert-Begrenzung).<ref type="graphic" target="#gattungssignal_2022_t8"/></desc>
+            <desc type="table" xml:id="tab9"><ref target="#tab009">Tab. 9</ref>: Test D1, Erkennungsquotentest mit fünf Vergleichstexten der
+               Zielklasse, ABE, BIL, GES.<ref type="graphic" target="#gattungssignal_2022_t9"/></desc>
+            <desc type="table" xml:id="tab10"><ref target="#tab010">Tab. 10</ref>: Test D2, ARI-Test mit 2 × 10 Distraktortexten, ABE, BIL, GES.<ref type="graphic" target="#gattungssignal_2022_t10"/></desc>
+            <desc type="table" xml:id="tab11"><ref target="#tab011">Tab. 11</ref>: Test D2, ARI-Test mit 2 × 10 Distraktortexten, Einzelwerte, ABE,
+               BIL, GES.<ref type="graphic" target="#gattungssignal_2022_t11"/></desc>
+            <desc type="table" xml:id="tab12"><ref target="#tab012">Tab. 12</ref>: Test D3, ARI-Test mit 2 × 5 Distraktortexten, ABE, BIL, GES.<ref type="graphic" target="#gattungssignal_2022_t12"/></desc>
+            <desc type="table" xml:id="tab13"><ref target="#tab013">Tab. 13</ref>: Test D4, ARI-Test mit 2 × 10 Distraktortexten und großem
+               MFW-Bereich, Volltexte, ABE, BIL, GES.<ref type="graphic" target="#gattungssignal_2022_t13"/></desc>
+            <desc type="table" xml:id="tab14"><ref target="#tab014">Tab. 14</ref>: Test D5, ARI-Test mit 2 × 10 Distraktortexten und großem
+               MFW-Bereich, Bag-of-Words (20.000 Wortformen), ABE, BIL, GES.<ref type="graphic" target="#gattungssignal_2022_t14"/></desc>
+            <desc type="table" xml:id="tab15"><ref target="#tab015">Tab. 15</ref>: Auszug aus den textsortenspezifischen Gute-Wörter-Listen: Jeweils
+               100 Wortformen mit den höchsten Level-2-Differenzen.<ref type="graphic" target="#gattungssignal_2022_t15"/></desc>
+            <desc type="table" xml:id="tab16"><ref target="#tab016">Tab. 16</ref>: Duplikate in den Listen der Guten Wörter.<ref type="graphic" target="#gattungssignal_2022_t16"/></desc>
+         </div>
+      </body>
+   </text>
+</TEI>
-- 
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