Span-based Joint Entity and Relation Extraction with Attention-based Span-specific and Contextual Semantic Representations

Bin Ji, Jie Yu, Shasha Li, Jun Ma, Qingbo Wu, Yusong Tan, Huijun Liu

Abstract
Span-based joint extraction models have shown their efficiency on entity recognition and relation extraction. These models regard text spans as candidate entities and span tuples as candidate relation tuples. Span semantic representations are shared in both entity recognition and relation extraction, while existing models cannot well capture semantics of these candidate entities and relations. To address these problems, we introduce a span-based joint extraction framework with attention-based semantic representations. Specially, attentions are utilized to calculate semantic representations, including span-specific and contextual ones. We further investigate effects of four attention variants in generating contextual semantic representations. Experiments show that our model outperforms previous systems and achieves state-of-the-art results on ACE2005, CoNLL2004 and ADE.
Anthology ID:
2020.coling-main.8
Volume:
Proceedings of the 28th International Conference on Computational Linguistics
Month:
December
Year:
2020
Address:
Barcelona, Spain (Online)
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
88–99
Language:
URL:
https://aclanthology.org/2020.coling-main.8
DOI:
10.18653/v1/2020.coling-main.8
Bibkey:
Cite (ACL):
Bin Ji, Jie Yu, Shasha Li, Jun Ma, Qingbo Wu, Yusong Tan, and Huijun Liu. 2020. Span-based Joint Entity and Relation Extraction with Attention-based Span-specific and Contextual Semantic Representations. In Proceedings of the 28th International Conference on Computational Linguistics, pages 88–99, Barcelona, Spain (Online). International Committee on Computational Linguistics.
Cite (Informal):
Span-based Joint Entity and Relation Extraction with Attention-based Span-specific and Contextual Semantic Representations (Ji et al., COLING 2020)
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