From 7a9177a7477814e5faf008bac9b07ee60f4f081b Mon Sep 17 00:00:00 2001
From: Jonathan <schimpf@hab.de>
Date: Thu, 17 Nov 2022 09:34:27 +0100
Subject: [PATCH] dimpel angelegt

---
 2022_009_dimpel/gattungssignal_2022_001.png  |  Bin 0 -> 59920 bytes
 2022_009_dimpel/gattungssignal_2022_002.png  |  Bin 0 -> 50079 bytes
 2022_009_dimpel/gattungssignal_2022_003.png  |  Bin 0 -> 43901 bytes
 2022_009_dimpel/gattungssignal_2022_004.png  |  Bin 0 -> 59295 bytes
 2022_009_dimpel/gattungssignal_2022_005.png  |  Bin 0 -> 58519 bytes
 2022_009_dimpel/gattungssignal_2022_006.png  |  Bin 0 -> 63357 bytes
 2022_009_dimpel/gattungssignal_2022_007.png  |  Bin 0 -> 62396 bytes
 2022_009_dimpel/gattungssignal_2022_008.png  |  Bin 0 -> 44438 bytes
 2022_009_dimpel/gattungssignal_2022_009.png  |  Bin 0 -> 65626 bytes
 2022_009_dimpel/gattungssignal_2022_010.png  |  Bin 0 -> 74214 bytes
 2022_009_dimpel/gattungssignal_2022_011.png  |  Bin 0 -> 59517 bytes
 2022_009_dimpel/gattungssignal_2022_v1_0.pdf |  Bin 0 -> 145413 bytes
 2022_009_dimpel/gattungssignal_2022_v1_0.xml | 3606 ++++++++++++++++++
 13 files changed, 3606 insertions(+)
 create mode 100644 2022_009_dimpel/gattungssignal_2022_001.png
 create mode 100644 2022_009_dimpel/gattungssignal_2022_002.png
 create mode 100644 2022_009_dimpel/gattungssignal_2022_003.png
 create mode 100644 2022_009_dimpel/gattungssignal_2022_004.png
 create mode 100644 2022_009_dimpel/gattungssignal_2022_005.png
 create mode 100644 2022_009_dimpel/gattungssignal_2022_006.png
 create mode 100644 2022_009_dimpel/gattungssignal_2022_007.png
 create mode 100644 2022_009_dimpel/gattungssignal_2022_008.png
 create mode 100644 2022_009_dimpel/gattungssignal_2022_009.png
 create mode 100644 2022_009_dimpel/gattungssignal_2022_010.png
 create mode 100644 2022_009_dimpel/gattungssignal_2022_011.png
 create mode 100644 2022_009_dimpel/gattungssignal_2022_v1_0.pdf
 create mode 100644 2022_009_dimpel/gattungssignal_2022_v1_0.xml

diff --git a/2022_009_dimpel/gattungssignal_2022_001.png b/2022_009_dimpel/gattungssignal_2022_001.png
new file mode 100644
index 0000000000000000000000000000000000000000..0b52316496b684b3a0ce9d6c72cf1f20e34f3515
GIT binary patch
literal 59920
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diff --git a/2022_009_dimpel/gattungssignal_2022_006.png b/2022_009_dimpel/gattungssignal_2022_006.png
new file mode 100644
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diff --git a/2022_009_dimpel/gattungssignal_2022_v1_0.pdf b/2022_009_dimpel/gattungssignal_2022_v1_0.pdf
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diff --git a/2022_009_dimpel/gattungssignal_2022_v1_0.xml b/2022_009_dimpel/gattungssignal_2022_v1_0.xml
new file mode 100644
index 0000000..ab12ff0
--- /dev/null
+++ b/2022_009_dimpel/gattungssignal_2022_v1_0.xml
@@ -0,0 +1,3606 @@
+<?xml version="1.0" encoding="utf-8"?>
+<?xml-model href="https://www.zfdg.de/sites/default/files/schema/tei_zfdg.rnc" type="application/relax-ng-compact-syntax"
+    ?>
+<TEI xmlns="http://www.tei-c.org/ns/1.0" xmlns:html="http://www.w3.org/1999/html"
+   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>
+                        <resp>
+                           <persName>
+                              <name role="marc_aut">
+                                 <forename>Friedrich Michael</forename>
+                                 <surname>Dimpel</surname>
+                              </name>
+                              <email>mail@dimpel.de</email>
+                              <idno type="gnd">1111656460</idno>
+                              <idno type="orcid">0000-0003-4833-4897</idno>
+                           </persName>
+                        </resp>
+                        <orgName>Friedrich-Alexander-Universität Erlangen-Nürnberg</orgName>
+                     </respStmt>
+                     <idno type="doi">10.17175/2022_009</idno>
+                     <idno type="ppn">1819369722</idno>
+                     <idno type="zfdg">2022.009</idno>
+                     <idno type="url">https://www.zfdg.de/node/379</idno>
+                     <date when="2022-11-17">17.11.2022</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>Transformation der Word Vorlage nach TEI</resp>
+                        <persName/>
+                        <name role="marc_trc">
+                           <surname>Baumgarten</surname>
+                           <forename>Marcus</forename>
+                           <idno type="gnd">1192832655</idno>
+                        </name>
+                     </respStmt>
+                     <availability status="free">
+                        <p>Available at <ref target="https://www.zfdg.de">https://www.zfdg.de</ref>
+                        </p>
+                     </availability>
+                     <biblScope unit="year">2022</biblScope>
+                     <biblScope unit="artikel">xx</biblScope>
+                  </monogr>
+               </biblStruct>
+            </title>
+         </titleStmt>
+         <editionStmt>
+            <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>
+         </publicationStmt>
+         <sourceDesc>
+            <p>Einreichung als Fachartikel in der ZfdG durch die Autor*innen</p>
+         </sourceDesc>
+      </fileDesc>
+      <encodingDesc>
+         <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="2022-10-25">25.10.2022</date>
+            </p>
+         </editorialDecl>
+      </encodingDesc>
+      <profileDesc>
+         <creation>Einreichung als Artikel der Zeitschrift für digitale
+            Geisteswissenschaften</creation>
+         <langUsage>
+            <language ident="de">Text in Deutsch</language>
+            <language ident="de">Abstract in Deutsch</language>
+            <language ident="en">Abstract in 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/>
+      </revisionDesc>
+   </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 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 al. 2016a</ref>, S. 158.
+                  <ref type="bibliography" target="#hettinger_et_al_genre_2015">Hettinger et 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 al. 2016b</ref>. <ref type="bibliography" target="#kim_et_al_investigating_2017">Kim et 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="#ardanuy_sporleder_clustering_2014">Ardanuy / Sporleder 2014</ref>; <ref type="bibliography" target="#underwood_et_al_mapping_2013">Underwood et 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 al. 2010</ref>; <ref type="bibliography" target="#stamatatos_et_al_categorization_2000">Stamatatos et al. 2000</ref>; <ref type="bibliography" target="#kessler_et_al_automatic_1997">Kessler et al.
+                     1997</ref>.</note> Während bei Texten des*der gleichen Autors*in trotz aller
+               Veränderungen im Laufe der Schaffensperiode des*der gleichen Autors*in 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 – 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 – 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 – 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 verwenden.</p>
+            </div>
+            <div>
+               <p></p>
+               <p></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.</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 – 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 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 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 und als 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. 96–120.</note>; andernorts beruft sich
+               Selbmann auf Benno von Wiese, der <quote>die Epigonen [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 Preprocessing-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% der <term type="dh"
+                  >Token</term> entfernt. </p>
+            </div>
+            <div>
+               <p></p>
+               <p></p>
+               <p></p>
+               <p></p>
+            </div>
+            <div type="chapter">
+               <head>3. Gute Wörter berechnen – 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 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 –
+               bei Autorschaftsfragen ist also ein Text von dem*der Autor*in im Vergleichskorpus,
+               von dem*der 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% 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% auf 5.473 Wortformen. Aus Rechenzeitgründen wurden Wortformen nach
+                  75.000 Wortformen nicht mehr berücksichtigt (Cutoff) – 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 – also innerhalb
+               der Zielgattung – 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 – 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 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 – 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 – sie liegt hinsichtlich ihrer Länge im
+               Mittelfeld – 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 – wie bei den kürzeren Komödien und
+               Tragödien – 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% (A) bzw.
+               bei 33% (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. 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 – wie so oft
+                  im Bereich der Digital Humanities – 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 – 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 dieses 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 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% 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% 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 – 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% (A) bzw. 31% (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</cell>
+                  <cell>300</cell>
+                  <cell>400</cell>
+                  <cell/>
+               </row>
+               <row>
+                  <cell>Gute &amp; ZWB</cell>
+                  <cell>57,3</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</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>
+               <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. Abkürzungen: Gute: Mit Gute-Wörter-Liste / ZWB: Mit Z-Wert-Begrenzung auf 1.64 / Basis: Ohne Gute-Wörter-Liste und ohne Z-Wert-Begrenzung.<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 – dort werden Werte &gt;90% erreicht, selbst wenn sich nur ein
+               Text des*der Zielautors*in im Vergleichskorpus befindet.<note type="footnote"> Vgl.
+                  etwa <ref type="bibliography" target="#Büttner_et_al_delta_2017">Büttner et 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</cell>
+                     
+                     <cell>300</cell>
+                     
+                     <cell>400</cell>
+                     
+                  </row>
+                  <row>
+                     
+                     <cell>ARI Gute &amp; ZWB</cell>
+                     
+                     <cell>
+                        <hi rend="bold">0,34</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</cell>
+                     
+                     <cell>0,25</cell>
+                     
+                     <cell>0,2</cell>
+                     
+                     <cell>0,25</cell>
+                     
+                  </row>
+                  <row>
+                     
+                     <cell/>
+                     
+                     <cell/>
+                     
+                     <cell/>
+                     
+                     <cell/>
+                     
+                  </row>
+                  <row>
+                     
+                     <cell>F1 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 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 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 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>
+                    <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. Abkürzungen: ARI: Adjusted Rand Index / F1: Precision und Recall kombiniert / Gute: Mit Gute-Wörter-Liste / ZWB: Mit Z-Wert-Begrenzung auf 1.64 / EQ: Erkennungsquote in % / FP: False-Positives-Quote in % (niedriger ist besser) / Basis: Ohne Gute-Wörter-Liste und ohne Z-Wert-Begrenzung / Diff: Differenzen zwischen Werten mit Gute-Wörter-Liste und mit Z-Wert-Begrenzung zum Basiswert.<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</cell>
+                     
+                     <cell>F1</cell>
+                     
+                     <cell>EQ</cell>
+                     
+                     <cell>FP</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>
+                  <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 – 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 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</cell>
+                     
+                     <cell>0,28</cell>
+                     
+                     <cell>0,31</cell>
+                     
+                     <cell>0,3</cell>
+                     
+                  </row>
+                  <row>
+                     
+                     <cell>F1</cell>
+                     
+                     <cell>0,64</cell>
+                     
+                     <cell>0,69</cell>
+                     
+                     <cell>0,69</cell>
+                     
+                  </row>
+                  <row>
+                     
+                     <cell>EQ</cell>
+                     
+                     <cell>53,3</cell>
+                     
+                     <cell>61,2</cell>
+                     
+                     <cell>61,2</cell>
+                     
+                  </row>
+                  <row>
+                     
+                     <cell>FP</cell>
+                     
+                     <cell>13,9</cell>
+                     
+                     <cell>15,0</cell>
+                     
+                     <cell>15,7</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 Gute &amp; ZWB</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</cell>
+                        <cell>0,19</cell>
+                        <cell>0,16</cell>
+                        <cell>0,19</cell>
+                     </row>
+                     <row>
+                        <cell/>
+                        <cell/>
+                        <cell/>
+                        <cell/>
+                     </row>
+                     <row>
+                        <cell>F1 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 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 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 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>
+                  <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 Gute &amp; ZWB</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</cell>
+                     
+                     <cell>0,5</cell>
+                     
+                     <cell>0,46</cell>
+                     
+                     <cell>0,56</cell>
+                     
+                  </row>
+                  <row>
+                     
+                     <cell/>
+                     
+                     <cell/>
+                     
+                     <cell/>
+                     
+                     <cell/>
+                     
+                  </row>
+                  <row>
+                     
+                     <cell>F1 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 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 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 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>
+                  <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 – 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 Gute &amp; ZWB</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</cell>
+                     <cell>0,45</cell>
+                     <cell>0,37</cell>
+                     <cell>0,41</cell>
+                  </row>
+                  <row>
+                     <cell/>
+                     <cell/>
+                     <cell/>
+                     <cell/>
+                  </row>
+                  <row>
+                     <cell>F1 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 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 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 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>
+                  <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 &amp; ZWB</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</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> 
+               <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 Gute &amp; ZWB</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</cell>
+                     
+                     <cell>0,04</cell>
+                     
+                     <cell>0,04</cell>
+                     
+                     <cell>0,07</cell>
+                     
+                  </row>
+                  <row>
+                     
+                     <cell/>
+                     
+                     <cell/>
+                     
+                     <cell/>
+                     
+                     <cell/>
+                     
+                  </row>
+                  <row>
+                     
+                     <cell>F1 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 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 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 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>
+                  <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</cell>
+                     
+                     <cell>F1</cell>
+                     
+                     <cell>EQ</cell>
+                     
+                     <cell>FP</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>
+                  <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 Gute &amp; ZWB</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</cell>
+                     <cell>-0,01</cell>
+                     <cell>-0,01</cell>
+                     <cell>0,02</cell>
+                  </row>
+                  <row>
+                     <cell/>
+                     <cell/>
+                     <cell/>
+                     <cell/>
+                  </row>
+                  <row>
+                     <cell>F1 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 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 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 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>
+                  <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% 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 G&amp;ZWB 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 G&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 G&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 G&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</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 G&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 G&amp;ZWN 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 G&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 G&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 G&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 G&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 G&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 G&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 G&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 G&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 G&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 G&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>
+                           <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 G&amp;ZWB 1,0</cell>
+                     
+                     <cell>0,14</cell>
+                     
+                     <cell>0,17</cell>
+                     
+                  </row>
+                  <row>
+                     
+                     <cell>F1 G&amp;ZWB 1,0</cell>
+                     
+                     <cell>0,66</cell>
+                     
+                     <cell>0,67</cell>
+                     
+                  </row>
+                  <row>
+                     
+                     <cell>EQ G&amp;ZWB 1,0</cell>
+                     
+                     <cell>64,7</cell>
+                     
+                     <cell>
+                        <hi rend="bold">64,2</hi>
+                     </cell>
+                     
+                  </row>
+                  <row>
+                     
+                     <cell>FP G&amp;ZWB 1,0</cell>
+                     
+                     <cell>31,01</cell>
+                     
+                     <cell>27,23</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 – 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% bei immerhin nur
+               12,4% 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 – 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 – 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>
+            <bibl xml:id="ardanuy_sporleder_clustering_2014">Mariona Coll Ardanuy / Caroline Sporleder: Structure-based Clustering of Novels.
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+               (CLfL). Hg. von Association for Computational Linguistics. (EACL 2014, Göteborg,
+               27.04.2014) Stroudsburg, PA, 2014, S. 31–39. DOI: <ref
+                  target="http://dx.doi.org/10.3115/v1/W14-0905">10.3115/v1/W14-0905</ref>
+            </bibl>
+            <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:
+               Zeitschrift für digitale Geisteswissenschaften 2 (2017). DOI: <ref
+                  target="http://dx.doi.org/10.17175/2017_006">10.17175/2017_006</ref>
+            </bibl>
+            <bibl xml:id="büttner_proisl_stilometrie_2016">Andreas Büttner / Thomas Proisl: Stilometrie interdisziplinär: Merkmalsselektion
+               zur Differenzierung zwischen Ãœbersetzer- und Fachvokabular. In: Modellierung,
+               Vernetzung, Visualisierung. Die Digital Humanities als fächerübergreifendes
+               Forschungsparadigma. DHd 2016. Konferenzabstracts. Hg. von Elisabeth Burr. (DHd: 3,
+               Leipzig, 07.–12.03.2016) Duisburg 2016, S. 70–74. DOI: <ref
+                  target="https://doi.org/10.5281/zenodo.3679331">10.5281/zenodo.3679331</ref> <ptr type="gbv" cRef="858156008"/></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. 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_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. 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. 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="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
+               2018, S. 168–173. DOI: <ref target="https://doi.org/10.5281/zenodo.3684897"
+                  >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. (= 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="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. 3,
+               S. 315–321. DOI: <ref target="https://doi.org/10.1093/llc/fqr031"
+                  >10.1093/llc/fqr031</ref> <ptr type="gbv" cRef="50563922X"/></bibl>
+            <bibl xml:id="evert_et_al_burrows_2016">Stefan Evert / Fotis Jannidis / Steffen Pielström / Isabella Reger / Christof
+               Schöch / Thorsten Vitt: Burrows’ Delta verstehen. In: Modellierung, Vernetzung,
+               Visualisierung. Die Digital Humanities als fächerübergreifendes Forschungsparadigma.
+               DHd 2016. Konferenzabstracts. Hg. von Elisabeth Burr. 2. überarbeitete und erweiterte
+               Ausgabe. (DHd: 3, Leipzig, 07.–12.03.2016) Duisburg 2016, S. 62–65. [<ref
+                  target="http://dhd2016.de">online</ref>] </bibl>
+            <bibl xml:id="fuchs_hybride_1997">Stephan Fuchs: Hybride Helden: Gwigalois und Willehalm. Beiträge zum Heldenbild
+               und zur Poetik des Romans im frühen 13. Jahrhundert. Heidelberg 1997. (= Frankfurter
+               Beiträge zur Germanistik, 31) <ptr type="gbv" cRef="274372053"/></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.
+                  26th International Conference. Hg. von Qiming Chen / Abdelkader Hameurlain / Farouk
+                  Toumani / Roland Wagner / Hendrik Decker. (DEXA: 26, Valencia, 01.–04.09.2015). Cham
+                  u. a. 2015, S. 249–253. DOI: <ref target="https://doi.org/10.1109/DEXA.2015.62"
+                     >10.1109/DEXA.2015.62</ref> <ptr type="gbv" cRef="1521531668"/></bibl>
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+                  Classification of Literary Subgenres. In: Modellierung, Vernetzung, Visualisierung.
+                  Die Digital Humanities als fächerübergreifendes Forschungsparadigma. DHd 2016.
+                  Konferenzabstracts. Hg. von Elisabeth Burr. (DHd: 3, Leipzig, 07.–12.03.2016)
+                  Duisburg 2016, S. 158–162. DOI: <ref target="https://doi.org/10.5281/zenodo.3679331"
+                     >10.5281/zenodo.3679331</ref> <ptr type="gbv" cRef="858156008"/></bibl>
+            <bibl xml:id="hettinger_et_al_significance_2016b">Lena Hettinger / Fotis Jannidis / Isabella Reger / Andreas Hotho (2016b):
+               Significance Testing for the Classification of Literary Subgenres. In: Digital
+               Humanities 2016. Conference Abstracts. (DH 2016, Krakau, 11.-16.07.2016) Krakau 2016.
+                  [<ref target="https://dh2016.adho.org/abstracts/173">online</ref>]</bibl>
+<bibl xml:id="kessler_et_al_automatic_1997">Brett Kessler / Geoffrey Nunberg / Hinrich Schutze: Automatic Detection of Text
+               Genre. In: 35th Annual Meeting of the Association for Computational Linguistics and
+               8th Conference of the European Chapter of the Association for Computational
+               Linguistics. (ACL: 35 - EACL '97, Madrid, 07. –12.07.1997) Morristown, NJ 1997,
+               S. 32–38. DOI: <ref target="http://dx.doi.org/10.3115/976909.979622"
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+            <bibl xml:id="kim_et_al_investigating_2017">Evgeny Kim / Sebastian Padó / Roman Klinger: Investigating the Relationship
+               between Literary Genres and Emotional Plot Development. In: Proceedings of the Joint
+               SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences,
+               Humanities and Literature. Hg. von Beatrice Alex / Stefania Degaetano-Ortlieb / Anna
+               Feldman / Anna Kazantseva / Nils Reiter / Stan Szpakowicz. (SIGHUM: 11, Vancouver,
+               04.08.2017) Stroudsburg, PA, 2017, S. 17–26. DOI: <ref
+                  target="http://dx.doi.org/10.18653/v1/W17-2203">10.18653/v1/W17-2203</ref>
+            </bibl>
+               <bibl xml:id="schoech_corneille_2014">Christof Schöch: Corneille, Molière et les autres. Stilometrische Analysen zu
+                  Autorschaft und Gattungszugehörigkeit im französischen Theater der Klassik. In:
+                  Literaturwissenschaft im digitalen Medienwandel. Hg. von Christof Schöch / Lars
+                  Schneider. Berlin 2014, S. 130–157. (= Philologie im Netz / Beiheft, 7) PDF. [<ref
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+            <bibl xml:id="schoech_computational_2020">Christof Schöch: Computational Genre Analysis. In: Digital Humanities for Literary
+               Studies: Methods, Tools &amp; Practices. Hg. von James O'Sullivan. College Station,
+               TX 2020. Preprint. PDF. [<ref
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+                  >online</ref>] </bibl>
+            <bibl xml:id="schulz_poetik_2000">Armin Schulz: Poetik des Hybriden. Schema, Variation und intertextuelle
+               Kombinatorik in der Minne- und Aventiureepik: ‚Willehalm von Orlens‘ – ‚Partonopier
+               und Meliur‘ – ‚Wilhelm von Österreich‘ – ‚Die schöne Magelone‘. Berlin 2000. (=
+               Philologische Studien und Quellen, 161) <ptr type="gbv" cRef="313490295"/></bibl>
+            <bibl xml:id="selbmann_bildungsroman_1994">Rolf Selbmann: Der deutsche Bildungsroman. 2., überarbeitete und erweiterte
+               Auflage. Stuttgart u. a. 1994. (= Sammlung Metzler, 214) <ptr type="gbv" cRef="147843162"/></bibl>
+            <bibl xml:id="sharoff_et_al_babel_2010">Serge Sharoff / Zhili Wu / Katja Markert: The Web Library of Babel: evaluating
+               genre collections. In: Proceedings of the 7th International Conference on Language
+               Resources and Evaluation. Hg. von Nicoletta Calzolari / Khalid Choukri / Bente
+               Maegaard / Joseph Mariani / Jan Odijk / Stelios Piperidis / Mike Rosner / Daniel
+               Tapias. (LREC’10: 7, Valetta, 17.-23.05.2010) Paris 2010. PDF. [<ref
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+               categorization in terms of genre and author. In: Computational Linguistics 26 (2000),
+               S. 471–495. DOI: <ref target="https://doi.org/10.1162/089120100750105920"
+                  >10.1162/089120100750105920</ref>
+            </bibl>
+            <bibl xml:id="ulb-muenster_streit_2022">Der Streit um die Birne. Autorschafts-Attributionstest mit Burrows’ Delta und dessen
+               Optimierung für Kurztexte am Beispiel der ‚Halben Birne‘ des Konrad von Würzburg:
+               Anhang – Dimpel: Gute Wörter und Level-2-Differenzen bei Delta. Hg. von ULB Münster.
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+               genres in structurally complex volumes. In: Proceedings of the IEEE International
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+               Konstanz und Wende. In: Zeitschrift für digitale Geisteswissenschaften 2 (2017). DOI:
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+            </bibl></listBibl>
+            </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.
+               Abkürzungen: Gute: Mit Gute-Wörter-Liste / ZWB: Mit Z-Wert-Begrenzung auf 1.64 /
+               Basis: Ohne Gute-Wörter-Liste und ohne Z-Wert-Begrenzung.<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.
+               Abkürzungen: ARI: Adjusted Rand Index / F1: Precision und Recall kombiniert / Gute:
+               Mit Gute-Wörter-Liste / ZWB: Mit Z-Wert-Begrenzung auf 1.64 / EQ: Erkennungsquote in
+               % / FP: False-Positives-Quote in % (niedriger ist besser) / Basis: Ohne
+               Gute-Wörter-Liste und ohne Z-Wert-Begrenzung / Diff: Differenzen zwischen Werten mit
+               Gute-Wörter-Liste und mit Z-Wert-Begrenzung zum Basiswert.<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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