Hierarchical Region Learning for Nested Named Entity Recognition

Xinwei Long, Shuzi Niu, Yucheng Li


Abstract
Named Entity Recognition (NER) is deeply explored and widely used in various tasks. Usually, some entity mentions are nested in other entities, which leads to the nested NER problem. Leading region based models face both the efficiency and effectiveness challenge due to the high subsequence enumeration complexity. To tackle these challenges, we propose a hierarchical region learning framework to automatically generate a tree hierarchy of candidate regions with nearly linear complexity and incorporate structure information into the region representation for better classification. Experiments on benchmark datasets ACE-2005, GENIA and JNLPBA demonstrate competitive or better results than state-of-the-art baselines.
Anthology ID:
2020.findings-emnlp.430
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2020
Month:
November
Year:
2020
Address:
Online
Venues:
EMNLP | Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
4788–4793
Language:
URL:
https://aclanthology.org/2020.findings-emnlp.430
DOI:
10.18653/v1/2020.findings-emnlp.430
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2020.findings-emnlp.430.pdf
Data
GENIA