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.
@inproceedings{ Zehe2021,
Title = {{Detecting Scenes in Fiction: A new Segmentation Task}},
Author = { Albin Zehe and Leonard Konle and Lea Dümpelmann and Evelyn Gius and Andreas Hotho and Fotis Jannidis and Lucas Kaufmann and Markus Krug and Frank Puppe and Nils Reiter and Annekea Schreiber and Nathalie Wiedmer },
Booktitle = {{Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume}},
Pages = { 3167-3177 },
Publisher = { Association for Computational Linguistics },
Month = { April },
Doi = { 10.18653/v1/2021.eacl-main.276 },
Year = { 2021 }
}
TY -
TI - Detecting Scenes in Fiction: A new Segmentation Task
AU - Albin Zehe
AU - Leonard Konle
AU - Lea Dümpelmann
AU - Evelyn Gius
AU - Andreas Hotho
AU - Fotis Jannidis
AU - Lucas Kaufmann
AU - Markus Krug
AU - Frank Puppe
AU - Nils Reiter
AU - Annekea Schreiber
AU - Nathalie Wiedmer
PY - 2021
DO - 10.18653/v1/2021.eacl-main.276
PB - Association for Computational Linguistics
J2 - Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
ER -