@inproceedings{gupta-etal-2024-coreference,
title = "Coreference in Long Documents using Hierarchical Entity Merging",
author = "Gupta, Talika and
Hatzel, Hans Ole and
Biemann, Chris",
editor = "Bizzoni, Yuri and
Degaetano-Ortlieb, Stefania and
Kazantseva, Anna and
Szpakowicz, Stan",
booktitle = "Proceedings of the 8th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature (LaTeCH-CLfL 2024)",
month = mar,
year = "2024",
address = "St. Julians, Malta",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.latechclfl-1.2",
pages = "11--17",
abstract = "Current top-performing coreference resolution approaches are limited with regard to the maximum length of texts they can accept. We explore a recursive merging technique of entities that allows us to apply coreference models to texts of arbitrary length, as found in many narrative genres. In experiments on established datasets, we quantify the drop in resolution quality caused by this approach. Finally, we use an under-explored resource in the form of a fully coreference-annotated novel to illustrate our model{'}s performance for long documents in practice. Here, we achieve state-of-the-art performance, outperforming previous systems capable of handling long documents.",
}
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<abstract>Current top-performing coreference resolution approaches are limited with regard to the maximum length of texts they can accept. We explore a recursive merging technique of entities that allows us to apply coreference models to texts of arbitrary length, as found in many narrative genres. In experiments on established datasets, we quantify the drop in resolution quality caused by this approach. Finally, we use an under-explored resource in the form of a fully coreference-annotated novel to illustrate our model’s performance for long documents in practice. Here, we achieve state-of-the-art performance, outperforming previous systems capable of handling long documents.</abstract>
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%0 Conference Proceedings
%T Coreference in Long Documents using Hierarchical Entity Merging
%A Gupta, Talika
%A Hatzel, Hans Ole
%A Biemann, Chris
%Y Bizzoni, Yuri
%Y Degaetano-Ortlieb, Stefania
%Y Kazantseva, Anna
%Y Szpakowicz, Stan
%S Proceedings of the 8th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature (LaTeCH-CLfL 2024)
%D 2024
%8 March
%I Association for Computational Linguistics
%C St. Julians, Malta
%F gupta-etal-2024-coreference
%X Current top-performing coreference resolution approaches are limited with regard to the maximum length of texts they can accept. We explore a recursive merging technique of entities that allows us to apply coreference models to texts of arbitrary length, as found in many narrative genres. In experiments on established datasets, we quantify the drop in resolution quality caused by this approach. Finally, we use an under-explored resource in the form of a fully coreference-annotated novel to illustrate our model’s performance for long documents in practice. Here, we achieve state-of-the-art performance, outperforming previous systems capable of handling long documents.
%U https://aclanthology.org/2024.latechclfl-1.2
%P 11-17
Markdown (Informal)
[Coreference in Long Documents using Hierarchical Entity Merging](https://aclanthology.org/2024.latechclfl-1.2) (Gupta et al., LaTeCHCLfL-WS 2024)
ACL
- Talika Gupta, Hans Ole Hatzel, and Chris Biemann. 2024. Coreference in Long Documents using Hierarchical Entity Merging. In Proceedings of the 8th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature (LaTeCH-CLfL 2024), pages 11–17, St. Julians, Malta. Association for Computational Linguistics.