Geometric Deep Learning Models for Linking Character Names in Novels

Marek Kubis


Abstract
The paper investigates the impact of using geometric deep learning models on the performance of a character name linking system. The neural models that contain graph convolutional layers are confronted with the models that include conventional fully connected layers. The evaluation is performed with respect to the perfect name boundaries obtained from the test set and in a more demanding end-to-end setting where the character name linking system is preceded by a named entity recognizer.
Anthology ID:
2020.latechclfl-1.15
Volume:
Proceedings of the The 4th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature
Month:
December
Year:
2020
Address:
Online
Venues:
CLFL | COLING | LaTeCH | LaTeCHCLfL
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
127–132
Language:
URL:
https://aclanthology.org/2020.latechclfl-1.15
DOI:
Bibkey:
Cite (ACL):
Marek Kubis. 2020. Geometric Deep Learning Models for Linking Character Names in Novels. In Proceedings of the The 4th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature, pages 127–132, Online. International Committee on Computational Linguistics.
Cite (Informal):
Geometric Deep Learning Models for Linking Character Names in Novels (Kubis, LaTeCHCLfL 2020)
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PDF:
https://aclanthology.org/2020.latechclfl-1.15.pdf