@inproceedings{procopio-etal-2023-entity,
title = "Entity Disambiguation with Entity Definitions",
author = "Procopio, Luigi and
Conia, Simone and
Barba, Edoardo and
Navigli, Roberto",
editor = "Vlachos, Andreas and
Augenstein, Isabelle",
booktitle = "Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics",
month = may,
year = "2023",
address = "Dubrovnik, Croatia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.eacl-main.93",
doi = "10.18653/v1/2023.eacl-main.93",
pages = "1297--1303",
abstract = "Local models have recently attained astounding performances in Entity Disambiguation (ED), with generative and extractive formulations being the most promising research directions. However, previous works have so far limited their studies to using, as the textual representation of each candidate, only its Wikipedia title. Although certainly effective, this strategy presents a few critical issues, especially when titles are not sufficiently informative or distinguishable from one another. In this paper, we address this limitation and investigate the extent to which more expressive textual representations can mitigate it. We evaluate our approach thoroughly against standard benchmarks in ED and find extractive formulations to be particularly well-suited to such representations. We report a new state of the art on 2 out of the 6 benchmarks we consider and strongly improve the generalization capability over unseen patterns. We release our code, data and model checkpoints at \url{https://github.com/SapienzaNLP/extend}.",
}
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<abstract>Local models have recently attained astounding performances in Entity Disambiguation (ED), with generative and extractive formulations being the most promising research directions. However, previous works have so far limited their studies to using, as the textual representation of each candidate, only its Wikipedia title. Although certainly effective, this strategy presents a few critical issues, especially when titles are not sufficiently informative or distinguishable from one another. In this paper, we address this limitation and investigate the extent to which more expressive textual representations can mitigate it. We evaluate our approach thoroughly against standard benchmarks in ED and find extractive formulations to be particularly well-suited to such representations. We report a new state of the art on 2 out of the 6 benchmarks we consider and strongly improve the generalization capability over unseen patterns. We release our code, data and model checkpoints at https://github.com/SapienzaNLP/extend.</abstract>
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%0 Conference Proceedings
%T Entity Disambiguation with Entity Definitions
%A Procopio, Luigi
%A Conia, Simone
%A Barba, Edoardo
%A Navigli, Roberto
%Y Vlachos, Andreas
%Y Augenstein, Isabelle
%S Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics
%D 2023
%8 May
%I Association for Computational Linguistics
%C Dubrovnik, Croatia
%F procopio-etal-2023-entity
%X Local models have recently attained astounding performances in Entity Disambiguation (ED), with generative and extractive formulations being the most promising research directions. However, previous works have so far limited their studies to using, as the textual representation of each candidate, only its Wikipedia title. Although certainly effective, this strategy presents a few critical issues, especially when titles are not sufficiently informative or distinguishable from one another. In this paper, we address this limitation and investigate the extent to which more expressive textual representations can mitigate it. We evaluate our approach thoroughly against standard benchmarks in ED and find extractive formulations to be particularly well-suited to such representations. We report a new state of the art on 2 out of the 6 benchmarks we consider and strongly improve the generalization capability over unseen patterns. We release our code, data and model checkpoints at https://github.com/SapienzaNLP/extend.
%R 10.18653/v1/2023.eacl-main.93
%U https://aclanthology.org/2023.eacl-main.93
%U https://doi.org/10.18653/v1/2023.eacl-main.93
%P 1297-1303
Markdown (Informal)
[Entity Disambiguation with Entity Definitions](https://aclanthology.org/2023.eacl-main.93) (Procopio et al., EACL 2023)
ACL
- Luigi Procopio, Simone Conia, Edoardo Barba, and Roberto Navigli. 2023. Entity Disambiguation with Entity Definitions. In Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics, pages 1297–1303, Dubrovnik, Croatia. Association for Computational Linguistics.