Type-enriched Hierarchical Contrastive Strategy for Fine-Grained Entity Typing

Xinyu Zuo, Haijin Liang, Ning Jing, Shuang Zeng, Zhou Fang, Yu Luo


Abstract
Fine-grained entity typing (FET) aims to deduce specific semantic types of the entity mentions in the text. Modern methods for FET mainly focus on learning what a certain type looks like. And few works directly model the type differences, that is, let models know the extent that which one type is different from others. To alleviate this problem, we propose a type-enriched hierarchical contrastive strategy for FET. Our method can directly model the differences between hierarchical types and improve the ability to distinguish multi-grained similar types. On the one hand, we embed type into entity contexts to make type information directly perceptible. On the other hand, we design a constrained contrastive strategy on the hierarchical structure to directly model the type differences, which can simultaneously perceive the distinguishability between types at different granularity. Experimental results on three benchmarks, BBN, OntoNotes, and FIGER show that our method achieves significant performance on FET by effectively modeling type differences.
Anthology ID:
2022.coling-1.212
Volume:
Proceedings of the 29th International Conference on Computational Linguistics
Month:
October
Year:
2022
Address:
Gyeongju, Republic of Korea
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
2405–2417
Language:
URL:
https://aclanthology.org/2022.coling-1.212
DOI:
Bibkey:
Cite (ACL):
Xinyu Zuo, Haijin Liang, Ning Jing, Shuang Zeng, Zhou Fang, and Yu Luo. 2022. Type-enriched Hierarchical Contrastive Strategy for Fine-Grained Entity Typing. In Proceedings of the 29th International Conference on Computational Linguistics, pages 2405–2417, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
Cite (Informal):
Type-enriched Hierarchical Contrastive Strategy for Fine-Grained Entity Typing (Zuo et al., COLING 2022)
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PDF:
https://aclanthology.org/2022.coling-1.212.pdf
Data
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