Bipartite Flat-Graph Network for Nested Named Entity Recognition

Ying Luo, Hai Zhao


Abstract
In this paper, we propose a novel bipartite flat-graph network (BiFlaG) for nested named entity recognition (NER), which contains two subgraph modules: a flat NER module for outermost entities and a graph module for all the entities located in inner layers. Bidirectional LSTM (BiLSTM) and graph convolutional network (GCN) are adopted to jointly learn flat entities and their inner dependencies. Different from previous models, which only consider the unidirectional delivery of information from innermost layers to outer ones (or outside-to-inside), our model effectively captures the bidirectional interaction between them. We first use the entities recognized by the flat NER module to construct an entity graph, which is fed to the next graph module. The richer representation learned from graph module carries the dependencies of inner entities and can be exploited to improve outermost entity predictions. Experimental results on three standard nested NER datasets demonstrate that our BiFlaG outperforms previous state-of-the-art models.
Anthology ID:
2020.acl-main.571
Volume:
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2020
Address:
Online
Editors:
Dan Jurafsky, Joyce Chai, Natalie Schluter, Joel Tetreault
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
6408–6418
Language:
URL:
https://aclanthology.org/2020.acl-main.571
DOI:
10.18653/v1/2020.acl-main.571
Bibkey:
Cite (ACL):
Ying Luo and Hai Zhao. 2020. Bipartite Flat-Graph Network for Nested Named Entity Recognition. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 6408–6418, Online. Association for Computational Linguistics.
Cite (Informal):
Bipartite Flat-Graph Network for Nested Named Entity Recognition (Luo & Zhao, ACL 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.acl-main.571.pdf
Video:
 http://slideslive.com/38929316
Code
 cslydia/BiFlaG
Data
ACE 2005GENIA