@inproceedings{bammanAnnotatedDatasetCoreference2020,
  title = {An {{Annotated Dataset}} of {{Coreference}} in {{English Literature}}},
  booktitle = {Proceedings of the {{Twelfth Language Resources}} and {{Evaluation Conference}}},
  author = {Bamman, David and Lewke, Olivia and Mansoor, Anya},
  editor = {Calzolari, Nicoletta and B{\'e}chet, Fr{\'e}d{\'e}ric and Blache, Philippe and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo, H{\'e}l{\`e}ne and Moreno, Asuncion and Odijk, Jan and Piperidis, Stelios},
  year = 2020,
  month = may,
  pages = {44--54},
  publisher = {European Language Resources Association},
  address = {Marseille, France},
  urldate = {2025-05-15},
  abstract = {We present in this work a new dataset of coreference annotations for works of literature in English, covering 29,103 mentions in 210,532 tokens from 100 works of fiction published between 1719 and 1922. This dataset differs from previous coreference corpora in containing documents whose average length (2,105.3 words) is four times longer than other benchmark datasets (463.7 for OntoNotes), and contains examples of difficult coreference problems common in literature. This dataset allows for an evaluation of cross-domain performance for the task of coreference resolution, and analysis into the characteristics of long-distance within-document coreference.},
  isbn = {979-10-95546-34-4},
  langid = {english},
  file = {/Users/hansole/Zotero/storage/47LQ8JE3/Bamman et al. - 2020 - An Annotated Dataset of Coreference in English Lit.pdf}
}

@inproceedings{beilharzLibriVoxDeEnCorpusGermantoEnglish2020,
  title = {{{LibriVoxDeEn}}: {{A Corpus}} for {{German-to-English Speech Translation}} and {{German Speech Recognition}}},
  shorttitle = {{{LibriVoxDeEn}}},
  booktitle = {Proceedings of the {{Twelfth Language Resources}} and {{Evaluation Conference}}},
  author = {Beilharz, Benjamin and Sun, Xin and Karimova, Sariya and Riezler, Stefan},
  editor = {Calzolari, Nicoletta and B{\'e}chet, Fr{\'e}d{\'e}ric and Blache, Philippe and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo, H{\'e}l{\`e}ne and Moreno, Asuncion and Odijk, Jan and Piperidis, Stelios},
  year = 2020,
  month = may,
  pages = {3590--3594},
  publisher = {European Language Resources Association},
  address = {Marseille, France},
  urldate = {2025-10-21},
  abstract = {We present a corpus of sentence-aligned triples of German audio, German text, and English translation, based on German audio books. The speech translation data consist of 110 hours of audio material aligned to over 50k parallel sentences. An even larger dataset comprising 547 hours of German speech aligned to German text is available for speech recognition. The audio data is read speech and thus low in disfluencies. The quality of audio and sentence alignments has been checked by a manual evaluation, showing that speech alignment quality is in general very high. The sentence alignment quality is comparable to well-used parallel translation data and can be adjusted by cutoffs on the automatic alignment score. To our knowledge, this corpus is to date the largest resource for German speech recognition and for end-to-end German-to-English speech translation.},
  isbn = {979-10-95546-34-4},
  langid = {english},
  file = {/Users/hansole/Zotero/storage/7T2EVP5S/Beilharz et al. - 2020 - LibriVoxDeEn A Corpus for German-to-English Speech Translation and German Speech Recognition.pdf}
}

@inproceedings{boeffardFullyAutomaticAnnotation2012,
  title = {Towards {{Fully Automatic Annotation}} of {{Audio Books}} for {{TTS}}},
  booktitle = {Proceedings of the {{Eighth International Conference}} on {{Language Resources}} and {{Evaluation}} ({{LREC}}'12)},
  author = {Boeffard, Olivier and Charonnat, Laure and Maguer, S{\'e}bastien Le and Lolive, Damien},
  editor = {Calzolari, Nicoletta and Choukri, Khalid and Declerck, Thierry and Do{\u g}an, Mehmet U{\u g}ur and Maegaard, Bente and Mariani, Joseph and Moreno, Asuncion and Odijk, Jan and Piperidis, Stelios},
  year = 2012,
  month = may,
  pages = {975--980},
  publisher = {European Language Resources Association (ELRA)},
  address = {Istanbul, Turkey},
  urldate = {2025-10-21},
  abstract = {Building speech corpora is a first and crucial step for every text-to-speech synthesis system. Nowadays, the use of statistical models implies the use of huge sized corpora that need to be recorded, transcribed, annotated and segmented to be usable. The variety of corpora necessary for recent applications (content, style, etc.) makes the use of existing digital audio resources very attractive. Among all available resources, audiobooks, considering their quality, are interesting. Considering this framework, we propose a complete acquisition, segmentation and annotation chain for audiobooks that tends to be fully automatic. The proposed process relies on a data structure, Roots, that establishes the relations between the different annotation levels represented as sequences of items. This methodology has been applied successfully on 11 hours of speech extracted from an audiobook. A manual check, on a part of the corpus, shows the efficiency of the process.},
  file = {/Users/hansole/Zotero/storage/T64X9L8A/Boeffard et al. - 2012 - Towards Fully Automatic Annotation of Audio Books for TTS.pdf}
}

@inproceedings{collardanuyLivingMachinesStudy2020,
  title = {Living {{Machines}}: {{A}} Study of Atypical Animacy},
  shorttitle = {Living {{Machines}}},
  booktitle = {Proceedings of the 28th {{International Conference}} on {{Computational Linguistics}}},
  author = {Coll Ardanuy, Mariona and Nanni, Federico and Beelen, Kaspar and Hosseini, Kasra and Ahnert, Ruth and Lawrence, Jon and McDonough, Katherine and Tolfo, Giorgia and Wilson, Daniel CS and McGillivray, Barbara},
  editor = {Scott, Donia and Bel, Nuria and Zong, Chengqing},
  year = 2020,
  month = dec,
  pages = {4534--4545},
  publisher = {International Committee on Computational Linguistics},
  address = {Barcelona, Spain (Online)},
  doi = {10.18653/v1/2020.coling-main.400},
  urldate = {2025-10-20},
  abstract = {This paper proposes a new approach to animacy detection, the task of determining whether an entity is represented as animate in a text. In particular, this work is focused on atypical animacy and examines the scenario in which typically inanimate objects, specifically machines, are given animate attributes. To address it, we have created the first dataset for atypical animacy detection, based on nineteenth-century sentences in English, with machines represented as either animate or inanimate. Our method builds on recent innovations in language modeling, specifically BERT contextualized word embeddings, to better capture fine-grained contextual properties of words. We present a fully unsupervised pipeline, which can be easily adapted to different contexts, and report its performance on an established animacy dataset and our newly introduced resource. We show that our method provides a substantially more accurate characterization of atypical animacy, especially when applied to highly complex forms of language use.},
  file = {/Users/hansole/Zotero/storage/WBMTMJ9T/Coll Ardanuy et al. - 2020 - Living Machines A study of atypical animacy.pdf}
}

@inproceedings{doddingtonAutomaticContentExtraction2004,
  title = {The {{Automatic Content Extraction}} ({{ACE}}) {{Program}} -- {{Tasks}}, {{Data}}, and {{Evaluation}}},
  booktitle = {Proceedings of the {{Fourth International Conference}} on {{Language Resources}} and {{Evaluation}} ({{LREC}}'04)},
  author = {Doddington, George and Mitchell, Alexis and Przybocki, Mark and Ramshaw, Lance and Strassel, Stephanie and Weischedel, Ralph},
  editor = {Lino, Maria Teresa and Xavier, Maria Francisca and Ferreira, F{\'a}tima and Costa, Rute and Silva, Raquel},
  year = 2004,
  month = may,
  publisher = {European Language Resources Association (ELRA)},
  address = {Lisbon, Portugal},
  urldate = {2025-10-15},
  file = {/Users/hansole/Zotero/storage/YSL54SYB/Doddington et al. - 2004 - The Automatic Content Extraction (ACE) Program – Tasks, Data, and Evaluation.pdf}
}

@inproceedings{fischerProgrammableCorporaIntroducing2019,
  title = {Programmable {{Corpora}}: {{Introducing DraCor}}, an {{Infrastructure}} for the {{Research}} on {{European Drama}}},
  booktitle = {Digital {{Humanities}} 2019: "{{Complexities}}" ({{DH2019}})},
  author = {Fischer, Frank and B{\"o}rner, Ingo and G{\"o}bel, Mathias and Hechtl, Angelika and Kittel, Christopher and Milling, Carsten and Trilcke, Peer},
  year = 2019,
  month = jul,
  publisher = {Zenodo},
  address = {Utrecht},
  doi = {10.5281/zenodo.4284002},
  abstract = {{$<$}p{$>$}DraCor (short for \&quot;drama corpora\&quot;) is a growing collection of TEI-encoded plays in (mostly) European languages. The project showcases the concept of \&quot;Programmable Corpora\&quot;, conceived as research ecosystems revolving around an API that provides access to bespoke slices of extracted data. DraCor also exposes a SPARQL endpoint to connect its semantic entities (authors, plays, characters, etc.) to the Linked Open Data cloud.{$<$}/p{$>$}},
  langid = {english}
}

@article{hagedornBearingBagofTalesOpen2022,
  title = {Bearing a {{Bag-of-Tales}}: {{An Open Corpus}} of {{Annotated Folktales}} for {{Reproducible Research}}},
  shorttitle = {Bearing a {{Bag-of-Tales}}},
  author = {Hagedorn, Joshua and Dar{\'a}nyi, S{\'a}ndor},
  year = 2022,
  month = jan,
  journal = {Journal of Open Humanities Data},
  doi = {10.5334/johd.78},
  urldate = {2025-10-20},
  abstract = {Motifs in folktales and myths have been identified and articulated by scholars, and the computational identification and discovery of such motifs is an area of ongoing research. Achieving this goal means meeting scientific requirements (that methods be comparable and replicable) and requirements for collaboration (that multi-disciplinary teams can reliably access data). To support those requirements, access to consistent reference datasets is needed. Unfortunately, these datasets are not openly available in a format that supports their use in data science. Here we report work in progress toward this goal, having converted the Ashliman Folktexts collection into a public dataset of annotated tale texts. The data can be accessed at doi.org/10.5281/zenodo.6575263.},
  langid = {english},
  file = {/Users/hansole/Zotero/storage/IRWGBKSC/Hagedorn and Darányi - 2022 - Bearing a Bag-of-Tales An Open Corpus of Annotated Folktales for Reproducible Research  Journal of.pdf}
}

@article{hatzelMachineLearningComputational2023,
  title = {Machine Learning in Computational Literary Studies},
  author = {Hatzel, Hans Ole and Stiemer, Haimo and Biemann, Chris and Gius, Evelyn},
  year = 2023,
  month = aug,
  journal = {it - Information Technology},
  volume = {65},
  number = {4-5},
  pages = {200--217},
  publisher = {De Gruyter Oldenbourg},
  issn = {2196-7032},
  doi = {10.1515/itit-2023-0041},
  urldate = {2023-08-25},
  abstract = {In this article, we provide an overview of machine learning as it is applied in computational literary studies, the field of computational analysis of literary texts and literature related phenomena. We survey a number of scientific publications for the machine learning methodology the scholars used and explain concepts of machine learning and natural language processing while discussing our findings. We establish that besides transformer-based language models, researchers still make frequent use of more traditional, feature-based machine learning approaches; possible reasons for this are to be found in the challenging application of modern methods to the literature domain and in the more transparent nature of traditional approaches. We shed light on how machine learning-based approaches are integrated into a research process, which often proceeds primarily from the non-quantitative, interpretative approaches of non-digital literary studies. Finally, we conclude that the application of large language models in the computational literary studies domain may simplify the application of machine learning methodology going forward, if adequate approaches for the analysis of literary texts are found.},
  copyright = {De Gruyter expressly reserves the right to use all content for commercial text and data mining within the meaning of Section 44b of the German Copyright Act.},
  langid = {english},
  keywords = {computational literary studies,forthesis,language models,machine learning,natural language processing,transformers},
  file = {/Users/hansole/Zotero/storage/46U9YDPP/Hatzel et al. - 2023 - Machine learning in computational literary studies.pdf}
}

@inproceedings{hatzelNarrativitatUndHandlung2023,
  title = {Narrativit\"at Und {{Handlung}}: {{Zum Verh\"altnis}} von {{Handlungszusammenfassungen}} Und Relevanten {{Ereignissen}}},
  shorttitle = {Narrativit\"at Und {{Handlung}}},
  booktitle = {{{DHd2023}}: {{Open Humanities}}, {{Open Culture}}},
  author = {Hatzel, Hans Ole and Gius, Evelyn and Stiemer, Haimo and Biemann, Chris},
  year = 2023,
  publisher = {Zenodo},
  address = {Trier, Germany/Belval, Luxembourg},
  doi = {10.5281/zenodo.7715347},
  urldate = {2023-11-03},
  abstract = {Im Beitrag stellen wir den Vergleich zwischen als handlungsrelevant markierten Passagen und Ereignissen bzw. Ereignishaftigkeit an. Im Fokus steht die Frage nach der Relevanz von Ereignissen in Erz\"ahltexten, welche in der Literaturwissenschaft vielfach und kontrovers verhandelt wird. Unser Ansatz sieht die Anwendung von N-Gram- und embedding-basierten Methoden vor, um semiprofessionelle, professionelle und nutzer:innengenerierte Handlungszusammenfassungen miteinander zu vergleichen. Mit einer von uns abgewandelten Form der Pyramiden-Methode - die urspr\"unglich zur Evaluation automatisch erzeugter Zusammenfassungen entwickelt wurde - k\"onnen wir eruieren, dass die semiprofessionell verfassten Zusammenfassungen in den von uns untersuchten Daten den st\"arksten Handlungsbezug aufweisen. Im Weiteren k\"onnen wir einen positiven Zusammenhang zwischen der Ereignishaftigkeit einzelner Text-Passagen und ihrer Erw\"ahnung in Zusammenfassungen best\"atigen. In Zukunft wollen wir \"uberpr\"ufen, auch auf Basis der hier vorgestellten Analysen, inwiefern zwischen grundlegenden Ereignistypen, die auch unter dem Konzept "Event I" subsumiert werden k\"onnen, und besonders erz\"ahlw\"urdigen Ereignissen bzw. so genannten Events II, eine Verbindung besteht. Ein Beitrag zur 9. Tagung des Verbands "Digital Humanities im deutschsprachigen Raum" - DHd 2023 Open Humanities Open Culture.},
  langid = {english},
  file = {/Users/hansole/Zotero/storage/PFMDY82B/Narrativität und Handlung Zum Verhältnis von Hand.pdf}
}

@inproceedings{hatzelScalableVerbBasedLiterary2025,
  title = {Scalable {{Verb-Based Literary Semantics}}},
  booktitle = {Proceedings of the {{Workshop}} on {{Computational Humanities Research}} ({{CHR}} 2025)},
  author = {Hatzel, Hans Ole and Stiemer, Haimo and Gius, Evelyn and Biemann, Chris},
  year = 2025,
  month = dec,
}

@inproceedings{ketschikNetzwerkanalysenNarrativerTexte2025,
  title = {Netzwerkanalysen Narrativer {{Texte}} - Ein {{Vorgehensmodell}}},
  booktitle = {{{DHd}} 2025: {{Under Construction}}},
  author = {Ketschik, Nora},
  year = 2025,
  month = apr,
  publisher = {Zenodo},
  address = {Bielefeld, Germany},
  doi = {10.5281/zenodo.15124132},
  urldate = {2025-05-15},
  abstract = {Die soziale Netzwerkanalyse ist in den Computational Literary Studies als Methode etabliert, mit der insbesondere Figurenbeziehungen in fiktionalen Welten exploriert und analysiert werden. W\"ahrend Netzwerkanalysen zu dramatischen Texten verbreitet und die Verfahren zur Datenerhebung erprobt sind, wird die Methode nach wie vor nur vereinzelt f\"ur die Analyse narrativer Texte verwendet. Das liegt vor allem daran, dass es um ein Vielfaches voraussetzungsreicher ist, die f\"ur Netzwerkanalysen ben\"otigten Daten aus narrativen Texten zu extrahieren. Die Komplexit\"at resultiert nicht nur aus Tasks wie Entit\"atenreferenzerkennung und Koreferenzresolution, sondern auch daraus, dass die Organisation des Erz\"ahlten (z.B. auf Erz\"ahlebenen) und die Kontexte von Figurennennungen (z.B. Exkurse und Figurenrede) ber\"ucksichtigt werden m\"ussen. Der vorliegende Beitrag versucht, diese methodische "L\"ucke" zu schlie\ss en, indem er ein modulares Vorgehensmodell entwickelt, das verschiedene Aspekte narrativer Texte integriert und geeignet ist, kookkurrenzbasierte Figurennetzwerke aus Erz\"ahltexten zu erstellen.},
  keywords = {Annotieren,benannte Entitaten (named entities),DHd2025,Figuren,Literatur,Methode,Methoden,Netzwerkanalyse,Paper,Vortrag: Methode},
  file = {/Users/hansole/Zotero/storage/5Z9R88R6/Ketschik - 2025 - Netzwerkanalysen narrativer Texte - ein Vorgehensm.pdf}
}

@inproceedings{nenkovaEvaluatingContentSelection2004,
  title = {Evaluating {{Content Selection}} in {{Summarization}}: {{The Pyramid Method}}},
  shorttitle = {Evaluating {{Content Selection}} in {{Summarization}}},
  booktitle = {Proceedings of the {{Human Language Technology Conference}} of the {{North American Chapter}} of the {{Association}} for {{Computational Linguistics}}: {{HLT-NAACL}} 2004},
  author = {Nenkova, Ani and Passonneau, Rebecca},
  year = 2004,
  month = may,
  pages = {145--152},
  publisher = {Association for Computational Linguistics},
  address = {Boston, Massachusetts, USA},
  urldate = {2022-06-10},
  file = {/Users/hansole/Zotero/storage/6MAVLQYJ/Nenkova and Passonneau - 2004 - Evaluating Content Selection in Summarization The.pdf}
}

@inproceedings{niDurationModelingGlobal2019,
  title = {Duration {{Modeling}} with {{Global Phoneme-Duration Vectors}}},
  booktitle = {Proc. {{Interspeech}} 2019},
  author = {Ni, Jinfu and Shiga, Yoshinori and Kawai, Hisashi},
  year = 2019,
  pages = {4465--4469},
  address = {Graz, Swizterland},
  doi = {10.21437/Interspeech.2019-2126},
  urldate = {2025-10-20}
}

@inproceedings{perriOneGraphRule2022a,
  title = {One {{Graph}} to {{Rule}} Them {{All}}: {{Using NLP}} and {{Graph Neural}}     {{Networks}} to Analyse {{Tolkien}}'s {{Legendarium}}},
  shorttitle = {One {{Graph}} to {{Rule}} Them {{All}}},
  booktitle = {Proceedings of the {{Computational Humanities Research}}   {{Conference}} 2022},
  author = {Perri, Vincenzo and Qarkaxhija, Lisi and Zehe, Albin and Hotho, Andreas and Scholtes, Ingo},
  editor = {Karsdorp, Folgert and Lassche, Alie and Nielbo, Kristoffer},
  year = 2022,
  month = dec,
  series = {{{CEUR Workshop Proceedings}}},
  volume = {3290},
  pages = {291--317},
  publisher = {CEUR},
  address = {Antwerp, Belgium},
  issn = {1613-0073},
  urldate = {2025-02-28},
  langid = {english},
  file = {/Users/hansole/Zotero/storage/UAAY49CJ/Perri et al. - 2022 - One Graph to Rule them All Using NLP and Graph Neural     Networks to analyse Tolkien's Legendarium.pdf}
}

@inproceedings{petersen-freyFinegrainedQuotationDetection2024,
  title = {Fine-Grained Quotation Detection and Attribution in {{German}} News Articles},
  booktitle = {Proceedings of the 20th {{Conference}} on {{Natural Language Processing}} ({{KONVENS}} 2024)},
  author = {{Petersen-Frey}, Fynn and Biemann, Chris},
  editor = {{Luz de Araujo}, Pedro Henrique and Baumann, Andreas and Gromann, Dagmar and Krenn, Brigitte and Roth, Benjamin and Wiegand, Michael},
  year = 2024,
  month = sep,
  pages = {196--208},
  publisher = {Association for Computational Linguistics},
  address = {Vienna, Austria},
  urldate = {2025-10-17},
  file = {/Users/hansole/Zotero/storage/IBM7QEGK/Petersen-Frey and Biemann - 2024 - Fine-grained quotation detection and attribution in German news articles.pdf}
}

@incollection{pichlerReflektierteTextanalyse2020,
  title = {{Reflektierte Textanalyse}},
  booktitle = {{Reflektierte algorithmische Textanalyse: Interdisziplin\"are(s) Arbeiten in der CRETA-Werkstatt}},
  author = {Pichler, Axel and Reiter, Nils},
  year = 2020,
  month = jul,
  pages = {43--60},
  publisher = {De Gruyter},
  doi = {10.1515/9783110693973-016},
  urldate = {2021-06-17},
  abstract = {Das Kapitel Reflektierte Textanalyse erschien in Reflektierte algorithmische Textanalyse auf Seite 43.},
  isbn = {978-3-11-069397-3},
  langid = {ngerman},
  file = {/Users/hansole/Zotero/storage/GTV7N9L9/Pichler und Reiter - 2020 - Reflektierte Textanalyse.pdf;/Users/hansole/Zotero/storage/UGY9YLL2/html.html}
}

@article{piperDataDrivenTheoryNarrativity2022,
  title = {Toward a {{Data-Driven Theory}} of {{Narrativity}}},
  author = {Piper, Andrew and Bagga, Sunyam},
  year = 2022,
  journal = {New Literary History},
  volume = {54},
  number = {1},
  pages = {879--901},
  publisher = {Johns Hopkins University Press},
  issn = {1080-661X},
  urldate = {2025-10-20},
  abstract = {In this essay, we provide a framework for the empirical testing of narrative theory using the process of machine learning and predictive modeling. Drawing on a collection of over thirteen-thousand passages from an array of different genres, our models suggest that a very small number of features are highly predictive of narrative communication and that these features strongly align with reader judgments. According to our models, narrativity can best be described by what we call the "distant worlds theory," where narrative communication is most strongly identified through the depiction of concretized actions of an agent set at a distance to the teller. These findings raise interesting questions with respect to the deictic and distanciating functions of narration as a cultural practice. Ultimately, we argue that predictive modeling can serve as a valuable tool in the literary critical toolkit to address the problems of theory validation, theory reduction, and theory development. Predictive modeling can help us move past expert opinion as the sole form of validation and gain confidence about the generalizability of our theories about literary behavior in the world.},
  file = {/Users/hansole/Zotero/storage/TAPYM4CB/Piper and Bagga - 2022 - Toward a Data-Driven Theory of Narrativity.pdf}
}

@inproceedings{roesigerCoreferenceLiteraryText2018,
  title = {Towards {{Coreference}} for {{Literary Text}}: {{Analyzing Domain-Specific Phenomena}}},
  shorttitle = {Towards {{Coreference}} for {{Literary Text}}},
  booktitle = {Proceedings of the {{Second Joint SIGHUM Workshop}} on {{Computational Linguistics}} for {{Cultural Heritage}}, {{Social Sciences}}, {{Humanities}} and {{Literature}}},
  author = {Roesiger, Ina and Schulz, Sarah and Reiter, Nils},
  year = 2018,
  month = aug,
  pages = {129--138},
  publisher = {Association for Computational Linguistics},
  address = {Santa Fe, New Mexico},
  urldate = {2020-09-28},
  abstract = {Coreference resolution is the task of grouping together references to the same discourse entity. Resolving coreference in literary texts could benefit a number of Digital Humanities (DH) tasks, such as analyzing the depiction of characters and/or their relations. Domain-dependent training data has shown to improve coreference resolution for many domains, e.g. the biomedical domain, as its properties differ significantly from news text or dialogue, on which automatic systems are typically trained. Literary texts could also benefit from corpora annotated with coreference. We therefore analyze the specific properties of coreference-related phenomena on a number of texts and give directions for the adaptation of annotation guidelines. As some of the adaptations have profound impact, we also present a new annotation tool for coreference, with a focus on enabling annotation of long texts with many discourse entities.},
  file = {/Users/hansole/Zotero/storage/7SX35DJ8/Roesiger et al. - 2018 - Towards Coreference for Literary Text Analyzing D.pdf}
}

@article{schochCreatingEuropeanLiterary2021,
  title = {Creating the {{European Literary Text Collection}} ({{ELTeC}}): {{Challenges}} and {{Perspectives}}},
  shorttitle = {Creating the {{European Literary Text Collection}} ({{ELTeC}})},
  author = {Sch{\"o}ch, Christof and Patras, Roxana and Erjavec, Toma{\v z} and Santos, Diana},
  year = 2021,
  month = may,
  journal = {Modern Languages Open},
  volume = {0},
  number = {1},
  pages = {25--44},
  doi = {10.3828/mlo.v0i0.364},
  urldate = {2025-10-20},
  abstract = {The aim of this contribution is to reflect on the process of building the multilingual European Literary Text Collection (ELTeC) that is being created in the framework of the networking project Distant Reading for European Literary History funded by COST (European Cooperation in Science and Technology). To provide some background, we briefly introduce the basic idea of ELTeC with a focus on the overall goals and intended usage scenarios. We then describe the collection composition principles that we have derived from the usage scenarios. In our discussion of the corpus-building process, we focus on collections of novels from four different literary traditions as components of ELTeC: French, Portuguese, Romanian, and Slovenian, selected from the more than twenty collections that are currently in preparation. For each collection, we describe some of the challenges we have encountered and the solutions developed while building ELTeC. In each case, the literary tradition, the history of the language, the current state of digitization of cultural heritage, the resources available locally, and the scholars' training level with regard to digitization and corpus building have been vastly different. How can we, in this context, hope to build comparable collections of novels that can usefully be integrated into a multilingual resource such as ELTeC and used in Distant Reading research? Based on our individual and collective experience with contributing to ELTeC, we end this contribution with some lessons learned regarding collaborative, multilingual corpus building.},
  langid = {american},
  file = {/Users/hansole/Zotero/storage/6QJBGEDX/Schöch et al. - 2021 - Creating the European Literary Text Collection (ELTeC) Challenges and Perspectives  Modern Languag.pdf}
}

@article{steigerTestsComparingElements1980,
  title = {Tests for Comparing Elements of a Correlation Matrix},
  author = {Steiger, James H.},
  year = 1980,
  journal = {Psychological Bulletin},
  volume = {87},
  number = {2},
  pages = {245--251},
  publisher = {American Psychological Association},
  address = {US},
  issn = {1939-1455},
  doi = {10.1037/0033-2909.87.2.245},
  abstract = {In psychological research, it is desirable to be able to make statistical comparisons between correlation coefficients measured on the same individuals. For example, an experimenter (E) may wish to assess whether 2 predictors correlate equally with a criterion variable. In another situation, the E may wish to test the hypothesis that an entire matrix of correlations has remained stable over time. The present article reviews the literature on such tests, points out some statistics that should be avoided, and presents a variety of techniques that can be used safely with medium to large samples. Several numerical examples are provided. (18 ref) (PsycInfo Database Record (c) 2025 APA, all rights reserved)},
  keywords = {Literature Review,Statistical Correlation,Statistical Tests},
  file = {/Users/hansole/Zotero/storage/R2DC2NJG/1980-08757-001.html}
}

@inproceedings{stiemerPauseImText2025,
  title = {Pause Im {{Text}}. {{Zur Exploration}} Semantisch Konditionierter {{Sprechpausen}} in {{H\"orb\"uchern}}},
  booktitle = {Book of {{Abstracts}} - {{DHd}} 2025},
  author = {Stiemer, Haimo and Hatzel, Hans Ole and Biemann, Chris and Gius, Evelyn},
  year = 2025,
  address = {Bielefeld, Germany},
  doi = {10.5281/zenodo.14943032},
  urldate = {2025-10-20},
  abstract = {In diesem Beitrag wird ein alternativer Ansatz der Segmentierung, die Zergliederung von Erz\"ahltexten mittels der in der Rezitation emergenten Sprechpausen diskutiert. Die Anzahl der Pausensetzungen und die Pausenl\"angen werden hierbei als von den Vortragenden ad hoc oder planm\"a\ss ig vorgenommene sinnhafte Segmentierungen verstanden. F\"ur unsere Untersuchung haben wir die Sprechfassungen von drei Erz\"ahltexten automatisch transkribiert und dabei zwischen professionellen und Laien-Lesungen unterschieden. Durch die Auswahl der eingesprochenen Texte sollte weiterhin sichergestellt werden, dass Vortr\"age von Prosatexten mit verschiedenen narrativen Profilen bzw. Charakteristika Gegenstand der Analyse sind. Sollten sich Sprechpausen im Fortgang als geeignete Analyseeinheiten erweisen, k\"onnten unsere \"Uberlegungen einen vielversprechenden Ansatz zur automatischen Textsegmentierung begr\"unden, mit welchem auch narrative Analysen von z. B. Figuren oder Plotstrukturen erfolgen k\"onnte.},
  langid = {english},
  file = {/Users/hansole/Zotero/storage/HMS2QVMC/Stiemer et al. - Pause im Text. Zur Exploration semantisch konditionierter Sprechpausen in Hörbüchern.pdf}
}

@inproceedings{szekelyEvaluatingExpressiveSpeech2012,
  title = {Evaluating Expressive Speech Synthesis from Audiobook Corpora for Conversational Phrases},
  booktitle = {Proceedings of the {{Eighth International Conference}} on {{Language Resources}} and {{Evaluation}} ({{LREC}}'12)},
  author = {Sz{\'e}kely, {\'E}va and Cabral, Joao Paulo and {Abou-Zleikha}, Mohamed and Cahill, Peter and {Carson-Berndsen}, Julie},
  editor = {Calzolari, Nicoletta and Choukri, Khalid and Declerck, Thierry and Do{\u g}an, Mehmet U{\u g}ur and Maegaard, Bente and Mariani, Joseph and Moreno, Asuncion and Odijk, Jan and Piperidis, Stelios},
  year = 2012,
  month = may,
  pages = {3335--3339},
  publisher = {European Language Resources Association (ELRA)},
  address = {Istanbul, Turkey},
  urldate = {2025-10-21},
  abstract = {Audiobooks are a rich resource of large quantities of natural sounding, highly expressive speech. In our previous research we have shown that it is possible to detect different expressive voice styles represented in a particular audiobook, using unsupervised clustering to group the speech corpus of the audiobook into smaller subsets representing the detected voice styles. These subsets of corpora of different voice styles reflect the various ways a speaker uses their voice to express involvement and affect, or imitate characters. This study is an evaluation of the detection of voice styles in an audiobook in the application of expressive speech synthesis. A further aim of this study is to investigate the usability of audiobooks as a language resource for expressive speech synthesis of utterances of conversational speech. Two evaluations have been carried out to assess the effect of the genre transfer: transmitting expressive speech from read aloud literature to conversational phrases with the application of speech synthesis. The first evaluation revealed that listeners have different voice style preferences for a particular conversational phrase. The second evaluation showed that it is possible for users of speech synthesis systems to learn the characteristics of a voice style well enough to make reliable predictions about what a certain utterance will sound like when synthesised using that voice style.},
  file = {/Users/hansole/Zotero/storage/33AH5F2K/Székely et al. - 2012 - Evaluating expressive speech synthesis from audiobook corpora for conversational phrases.pdf}
}

@article{underwoodWhyLiteraryTime2018,
  title = {Why {{Literary Time}} Is {{Measured}} in {{Minutes}}},
  author = {Underwood, Ted},
  year = 2018,
  journal = {ELH},
  volume = {85},
  number = {2},
  pages = {341--365},
  publisher = {Johns Hopkins University Press},
  issn = {1080-6547},
  urldate = {2025-10-20},
  file = {/Users/hansole/Zotero/storage/NHE65KTF/Underwood - 2018 - Why Literary Time is Measured in Minutes.pdf}
}

@inproceedings{vauthAutomatedEventAnnotation2021,
  title = {Automated {{Event Annotation}} in {{Literary Texts}}},
  booktitle = {{{CHR}} 2021: {{Computational Humanities Research Conference}}},
  author = {Vauth, Michael and Hatzel, Hans Ole and Gius, Evelyn and Biemann, Chris},
  year = 2021,
  month = nov,
  series = {{{CEUR Workshop Proceedings}}},
  volume = {2989},
  pages = {333--345},
  address = {Amsterdam, The Netherlands},
  abstract = {We approach the modeling of event structure of literary texts with narratological event concepts. A manually annotated corpus of 4 prose texts with event categories allows us to learn to automatically classify events using a transformer model, relying on a rule-based system in conjunction with a pretrained parser to identify events. For the evaluation of both manual and automated annotation, we use narrativity graphs, which capture the change in narrativity over the course of the text. In an exploratory analysis, we apply the event classifier in conjunction with graph-based narrativity metrics to a large literary corpus. We find that text length does neither influence the length of eventful passages nor the number of eventful passages in the beginnings of texts.},
  file = {/Users/hansole/Zotero/storage/K87JKU5A/Vauth et al. - Automated Event Annotation in Literary Texts.pdf}
}

@article{weberReadingPublicDomain2021,
  title = {"{{Reading}}" the {{Public Domain}}: {{Narrating}} and {{Listening}} to {{Librivox Audiobooks}}},
  shorttitle = {"{{Reading}}" the {{Public Domain}}},
  author = {Weber, Millicent},
  year = 2021,
  journal = {Book History},
  volume = {24},
  number = {1},
  pages = {209--243},
  publisher = {Johns Hopkins University Press},
  issn = {1529-1499},
  urldate = {2025-10-21},
  file = {/Users/hansole/Zotero/storage/8VY6K3NT/Weber - 2021 - Reading the Public Domain Narrating and Listening to Librivox Audiobooks.pdf}
}

@inproceedings{zeheDetectingScenesFiction2021,
  title = {Detecting {{Scenes}} in {{Fiction}}: {{A}} New {{Segmentation Task}}},
  booktitle = {Proceedings of the 16th {{Conference}} of the {{European Chapter}} of the {{Association}} for {{Computational Linguistics}}: {{Main Volume}}},
  author = {Zehe, Albin and Konle, Leonard and D{\"u}mpelmann, Lea Katharina and Gius, Evelyn and Hotho, Andreas and Jannidis, Fotis and Kaufmann, Lucas and Krug, Markus and Puppe, Frank and Reiter, Nils and Schreiber, Annekea and Wiedmer, Nathalie},
  year = 2021,
  month = apr,
  pages = {3167--3177},
  publisher = {Association for Computational Linguistics},
  address = {Online},
  abstract = {This paper introduces the novel task of scene segmentation on narrative texts and provides an annotated corpus, a discussion of the linguistic and narrative properties of the task and baseline experiments towards automatic solutions. A scene here is a segment of the text where time and discourse time are more or less equal, the narration focuses on one action and location and character constellations stay the same. The corpus we describe consists of German-language dime novels (550k tokens) that have been annotated in parallel, achieving an inter-annotator agreement of gamma = 0.7. Baseline experiments using BERT achieve an F1 score of 24\%, showing that the task is very challenging. An automatic scene segmentation paves the way towards processing longer narrative texts like tales or novels by breaking them down into smaller, coherent and meaningful parts, which is an important stepping stone towards the reconstruction of plot in Computational Literary Studies but also can serve to improve tasks like coreference resolution.},
  file = {/Users/hansole/Zotero/storage/ZEJCQJ27/Zehe et al. - Detecting Scenes in Fiction A new Segmentation Ta.pdf}
}
