Markus Krug


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Detecting Scenes in Fiction: A new Segmentation Task
Albin Zehe | Leonard Konle | Lea Katharina Dümpelmann | Evelyn Gius | Andreas Hotho | Fotis Jannidis | Lucas Kaufmann | Markus Krug | Frank Puppe | Nils Reiter | Annekea Schreiber | Nathalie Wiedmer
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

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.

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The FairyNet Corpus - Character Networks for German Fairy Tales
David Schmidt | Albin Zehe | Janne Lorenzen | Lisa Sergel | Sebastian Düker | Markus Krug | Frank Puppe
Proceedings of the 5th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature

This paper presents a data set of German fairy tales, manually annotated with character networks which were obtained with high inter rater agreement. The release of this corpus provides an opportunity of training and comparing different algorithms for the extraction of character networks, which so far was barely possible due to heterogeneous interests of previous researchers. We demonstrate the usefulness of our data set by providing baseline experiments for the automatic extraction of character networks, applying a rule-based pipeline as well as a neural approach, and find the neural approach outperforming the rule-approach in most evaluation settings.


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Rule-based Coreference Resolution in German Historic Novels
Markus Krug | Frank Puppe | Fotis Jannidis | Luisa Macharowsky | Isabella Reger | Lukas Weimar
Proceedings of the Fourth Workshop on Computational Linguistics for Literature