Sequence-to-Nuggets: Nested Entity Mention Detection via Anchor-Region Networks

Hongyu Lin, Yaojie Lu, Xianpei Han, Le Sun


Abstract
Sequential labeling-based NER approaches restrict each word belonging to at most one entity mention, which will face a serious problem when recognizing nested entity mentions. In this paper, we propose to resolve this problem by modeling and leveraging the head-driven phrase structures of entity mentions, i.e., although a mention can nest other mentions, they will not share the same head word. Specifically, we propose Anchor-Region Networks (ARNs), a sequence-to-nuggets architecture for nested mention detection. ARNs first identify anchor words (i.e., possible head words) of all mentions, and then recognize the mention boundaries for each anchor word by exploiting regular phrase structures. Furthermore, we also design Bag Loss, an objective function which can train ARNs in an end-to-end manner without using any anchor word annotation. Experiments show that ARNs achieve the state-of-the-art performance on three standard nested entity mention detection benchmarks.
Anthology ID:
P19-1511
Volume:
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2019
Address:
Florence, Italy
Editors:
Anna Korhonen, David Traum, Lluís Màrquez
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
5182–5192
Language:
URL:
https://aclanthology.org/P19-1511
DOI:
10.18653/v1/P19-1511
Bibkey:
Cite (ACL):
Hongyu Lin, Yaojie Lu, Xianpei Han, and Le Sun. 2019. Sequence-to-Nuggets: Nested Entity Mention Detection via Anchor-Region Networks. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 5182–5192, Florence, Italy. Association for Computational Linguistics.
Cite (Informal):
Sequence-to-Nuggets: Nested Entity Mention Detection via Anchor-Region Networks (Lin et al., ACL 2019)
Copy Citation:
PDF:
https://aclanthology.org/P19-1511.pdf
Code
 sanmusunrise/ARNs
Data
ACE 2005GENIA