@inproceedings{schmidt-etal-2021-fairynet,
title = "The {F}airy{N}et Corpus - Character Networks for {G}erman Fairy Tales",
author = {Schmidt, David and
Zehe, Albin and
Lorenzen, Janne and
Sergel, Lisa and
D{\"u}ker, Sebastian and
Krug, Markus and
Puppe, Frank},
editor = "Degaetano-Ortlieb, Stefania and
Kazantseva, Anna and
Reiter, Nils and
Szpakowicz, Stan",
booktitle = "Proceedings of the 5th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature",
month = nov,
year = "2021",
address = "Punta Cana, Dominican Republic (online)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.latechclfl-1.6",
doi = "10.18653/v1/2021.latechclfl-1.6",
pages = "49--56",
abstract = "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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%0 Conference Proceedings
%T The FairyNet Corpus - Character Networks for German Fairy Tales
%A Schmidt, David
%A Zehe, Albin
%A Lorenzen, Janne
%A Sergel, Lisa
%A Düker, Sebastian
%A Krug, Markus
%A Puppe, Frank
%Y Degaetano-Ortlieb, Stefania
%Y Kazantseva, Anna
%Y Reiter, Nils
%Y Szpakowicz, Stan
%S Proceedings of the 5th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature
%D 2021
%8 November
%I Association for Computational Linguistics
%C Punta Cana, Dominican Republic (online)
%F schmidt-etal-2021-fairynet
%X 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.
%R 10.18653/v1/2021.latechclfl-1.6
%U https://aclanthology.org/2021.latechclfl-1.6
%U https://doi.org/10.18653/v1/2021.latechclfl-1.6
%P 49-56
Markdown (Informal)
[The FairyNet Corpus - Character Networks for German Fairy Tales](https://aclanthology.org/2021.latechclfl-1.6) (Schmidt et al., LaTeCHCLfL 2021)
ACL
- David Schmidt, Albin Zehe, Janne Lorenzen, Lisa Sergel, Sebastian Düker, Markus Krug, and Frank Puppe. 2021. The FairyNet Corpus - Character Networks for German Fairy Tales. In Proceedings of the 5th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature, pages 49–56, Punta Cana, Dominican Republic (online). Association for Computational Linguistics.