Fine-Grained Evaluation for Entity Linking

Henry Rosales-Méndez, Aidan Hogan, Barbara Poblete


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.
Anthology ID:
D19-1066
Volume:
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:
November
Year:
2019
Address:
Hong Kong, China
Editors:
Kentaro Inui, Jing Jiang, Vincent Ng, Xiaojun Wan
Venues:
EMNLP | IJCNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
718–727
Language:
URL:
https://aclanthology.org/D19-1066
DOI:
10.18653/v1/D19-1066
Bibkey:
Cite (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.
Cite (Informal):
Fine-Grained Evaluation for Entity Linking (Rosales-Méndez et al., EMNLP-IJCNLP 2019)
Copy Citation:
PDF:
https://aclanthology.org/D19-1066.pdf