@inproceedings{rosales-mendez-etal-2019-fine,
title = "Fine-Grained Evaluation for Entity Linking",
author = "Rosales-M{\'e}ndez, Henry and
Hogan, Aidan and
Poblete, Barbara",
editor = "Inui, Kentaro and
Jiang, Jing and
Ng, Vincent and
Wan, Xiaojun",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-1066/",
doi = "10.18653/v1/D19-1066",
pages = "718--727",
abstract = "The Entity Linking (EL) task identifies entity mentions in a text corpus and associates them with an unambiguous identifier in a Knowledge Base. While much work has been done on the topic, we first present the results of a survey that reveal a lack of consensus in the community regarding what forms of mentions in a text and what forms of links the EL task should consider. We argue that no one definition of the Entity Linking task fits all, and rather propose a fine-grained categorization of different types of entity mentions and links. We then re-annotate three EL benchmark datasets {--} ACE2004, KORE50, and VoxEL {--} with respect to these categories. We propose a fuzzy recall metric to address the lack of consensus and conclude with fine-grained evaluation results comparing a selection of online EL systems."
}
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<abstract>The Entity Linking (EL) task identifies entity mentions in a text corpus and associates them with an unambiguous identifier in a Knowledge Base. While much work has been done on the topic, we first present the results of a survey that reveal a lack of consensus in the community regarding what forms of mentions in a text and what forms of links the EL task should consider. We argue that no one definition of the Entity Linking task fits all, and rather propose a fine-grained categorization of different types of entity mentions and links. We then re-annotate three EL benchmark datasets – ACE2004, KORE50, and VoxEL – with respect to these categories. We propose a fuzzy recall metric to address the lack of consensus and conclude with fine-grained evaluation results comparing a selection of online EL systems.</abstract>
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%0 Conference Proceedings
%T Fine-Grained Evaluation for Entity Linking
%A Rosales-Méndez, Henry
%A Hogan, Aidan
%A Poblete, Barbara
%Y Inui, Kentaro
%Y Jiang, Jing
%Y Ng, Vincent
%Y Wan, Xiaojun
%S Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F rosales-mendez-etal-2019-fine
%X The Entity Linking (EL) task identifies entity mentions in a text corpus and associates them with an unambiguous identifier in a Knowledge Base. While much work has been done on the topic, we first present the results of a survey that reveal a lack of consensus in the community regarding what forms of mentions in a text and what forms of links the EL task should consider. We argue that no one definition of the Entity Linking task fits all, and rather propose a fine-grained categorization of different types of entity mentions and links. We then re-annotate three EL benchmark datasets – ACE2004, KORE50, and VoxEL – with respect to these categories. We propose a fuzzy recall metric to address the lack of consensus and conclude with fine-grained evaluation results comparing a selection of online EL systems.
%R 10.18653/v1/D19-1066
%U https://aclanthology.org/D19-1066/
%U https://doi.org/10.18653/v1/D19-1066
%P 718-727
Markdown (Informal)
[Fine-Grained Evaluation for Entity Linking](https://aclanthology.org/D19-1066/) (Rosales-Méndez et al., EMNLP-IJCNLP 2019)
ACL
- Henry Rosales-Méndez, Aidan Hogan, and Barbara Poblete. 2019. Fine-Grained Evaluation for Entity Linking. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 718–727, Hong Kong, China. Association for Computational Linguistics.