The FairyNet Corpus - Character Networks for German Fairy Tales
D. Schmidt, A. Zehe, J. Lorenzen, L. Sergel, S. Düker, M. Krug, and F. Puppe. Proceedings of the 5th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature, page 49--56. Punta Cana, Dominican Republic (online), Association for Computational Linguistics, (November 2021)
DOI: 10.18653/v1/2021.latechclfl-1.6
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.
%0 Conference Paper
%1 schmidt2021fairynet
%A Schmidt, David
%A Zehe, Albin
%A Lorenzen, Janne
%A Sergel, Lisa
%A Düker, Sebastian
%A Krug, Markus
%A Puppe, Frank
%B Proceedings of the 5th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature
%C Punta Cana, Dominican Republic (online)
%D 2021
%I Association for Computational Linguistics
%K author:zehe characters dataset kallimachos myown networks nlp
%P 49--56
%R 10.18653/v1/2021.latechclfl-1.6
%T The FairyNet Corpus - Character Networks for German Fairy Tales
%U https://aclanthology.org/2021.latechclfl-1.6
%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.
@inproceedings{schmidt2021fairynet,
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.},
added-at = {2022-04-14T09:53:33.000+0200},
address = {Punta Cana, Dominican Republic (online)},
author = {Schmidt, David and Zehe, Albin and Lorenzen, Janne and Sergel, Lisa and D{\"u}ker, Sebastian and Krug, Markus and Puppe, Frank},
biburl = {https://www.bibsonomy.org/bibtex/2d43b073501e70081bc142ad8b8e1b42e/albinzehe},
booktitle = {Proceedings of the 5th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature},
doi = {10.18653/v1/2021.latechclfl-1.6},
interhash = {b91ecb63e253edef4864ff7ecdb35c60},
intrahash = {d43b073501e70081bc142ad8b8e1b42e},
keywords = {author:zehe characters dataset kallimachos myown networks nlp},
month = nov,
pages = {49--56},
publisher = {Association for Computational Linguistics},
timestamp = {2023-10-11T10:29:52.000+0200},
title = {The {F}airy{N}et Corpus - Character Networks for {G}erman Fairy Tales},
url = {https://aclanthology.org/2021.latechclfl-1.6},
year = 2021
}