@inproceedings{jahan-finlayson-2019-character,
title = "Character Identification Refined: A Proposal",
author = "Jahan, Labiba and
Finlayson, Mark",
editor = "Bamman, David and
Chaturvedi, Snigdha and
Clark, Elizabeth and
Fiterau, Madalina and
Iyyer, Mohit",
booktitle = "Proceedings of the First Workshop on Narrative Understanding",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W19-2402",
doi = "10.18653/v1/W19-2402",
pages = "12--18",
abstract = "Characters are a key element of narrative and so character identification plays an important role in automatic narrative understanding. Unfortunately, most prior work that incorporates character identification is not built upon a clear, theoretically grounded concept of character. They either take character identification for granted (e.g., using simple heuristics on referring expressions), or rely on simplified definitions that do not capture important distinctions between characters and other referents in the story. Prior approaches have also been rather complicated, relying, for example, on predefined case bases or ontologies. In this paper we propose a narratologically grounded definition of character for discussion at the workshop, and also demonstrate a preliminary yet straightforward supervised machine learning model with a small set of features that performs well on two corpora. The most important of the two corpora is a set of 46 Russian folktales, on which the model achieves an F1 of 0.81. Error analysis suggests that features relevant to the plot will be necessary for further improvements in performance.",
}
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<abstract>Characters are a key element of narrative and so character identification plays an important role in automatic narrative understanding. Unfortunately, most prior work that incorporates character identification is not built upon a clear, theoretically grounded concept of character. They either take character identification for granted (e.g., using simple heuristics on referring expressions), or rely on simplified definitions that do not capture important distinctions between characters and other referents in the story. Prior approaches have also been rather complicated, relying, for example, on predefined case bases or ontologies. In this paper we propose a narratologically grounded definition of character for discussion at the workshop, and also demonstrate a preliminary yet straightforward supervised machine learning model with a small set of features that performs well on two corpora. The most important of the two corpora is a set of 46 Russian folktales, on which the model achieves an F1 of 0.81. Error analysis suggests that features relevant to the plot will be necessary for further improvements in performance.</abstract>
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%0 Conference Proceedings
%T Character Identification Refined: A Proposal
%A Jahan, Labiba
%A Finlayson, Mark
%Y Bamman, David
%Y Chaturvedi, Snigdha
%Y Clark, Elizabeth
%Y Fiterau, Madalina
%Y Iyyer, Mohit
%S Proceedings of the First Workshop on Narrative Understanding
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F jahan-finlayson-2019-character
%X Characters are a key element of narrative and so character identification plays an important role in automatic narrative understanding. Unfortunately, most prior work that incorporates character identification is not built upon a clear, theoretically grounded concept of character. They either take character identification for granted (e.g., using simple heuristics on referring expressions), or rely on simplified definitions that do not capture important distinctions between characters and other referents in the story. Prior approaches have also been rather complicated, relying, for example, on predefined case bases or ontologies. In this paper we propose a narratologically grounded definition of character for discussion at the workshop, and also demonstrate a preliminary yet straightforward supervised machine learning model with a small set of features that performs well on two corpora. The most important of the two corpora is a set of 46 Russian folktales, on which the model achieves an F1 of 0.81. Error analysis suggests that features relevant to the plot will be necessary for further improvements in performance.
%R 10.18653/v1/W19-2402
%U https://aclanthology.org/W19-2402
%U https://doi.org/10.18653/v1/W19-2402
%P 12-18
Markdown (Informal)
[Character Identification Refined: A Proposal](https://aclanthology.org/W19-2402) (Jahan & Finlayson, WNU 2019)
ACL
- Labiba Jahan and Mark Finlayson. 2019. Character Identification Refined: A Proposal. In Proceedings of the First Workshop on Narrative Understanding, pages 12–18, Minneapolis, Minnesota. Association for Computational Linguistics.