HIT: Nested Named Entity Recognition via Head-Tail Pair and Token Interaction

Yu Wang, Yun Li, Hanghang Tong, Ziye Zhu


Abstract
Named Entity Recognition (NER) is a fundamental task in natural language processing. In order to identify entities with nested structure, many sophisticated methods have been recently developed based on either the traditional sequence labeling approaches or directed hypergraph structures. Despite being successful, these methods often fall short in striking a good balance between the expression power for nested structure and the model complexity. To address this issue, we present a novel nested NER model named HIT. Our proposed HIT model leverages two key properties pertaining to the (nested) named entity, including (1) explicit boundary tokens and (2) tight internal connection between tokens within the boundary. Specifically, we design (1) Head-Tail Detector based on the multi-head self-attention mechanism and bi-affine classifier to detect boundary tokens, and (2) Token Interaction Tagger based on traditional sequence labeling approaches to characterize the internal token connection within the boundary. Experiments on three public NER datasets demonstrate that the proposed HIT achieves state-of-the-art performance.
Anthology ID:
2020.emnlp-main.486
Volume:
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Month:
November
Year:
2020
Address:
Online
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
6027–6036
Language:
URL:
https://aclanthology.org/2020.emnlp-main.486
DOI:
10.18653/v1/2020.emnlp-main.486
Bibkey:
Cite (ACL):
Yu Wang, Yun Li, Hanghang Tong, and Ziye Zhu. 2020. HIT: Nested Named Entity Recognition via Head-Tail Pair and Token Interaction. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 6027–6036, Online. Association for Computational Linguistics.
Cite (Informal):
HIT: Nested Named Entity Recognition via Head-Tail Pair and Token Interaction (Wang et al., EMNLP 2020)
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PDF:
https://aclanthology.org/2020.emnlp-main.486.pdf
Video:
 https://slideslive.com/38938982