Toward Recognizing More Entity Types in NER: An Efficient Implementation using Only Entity Lexicons

Minlong Peng, Ruotian Ma, Qi Zhang, Lujun Zhao, Mengxi Wei, Changlong Sun, Xuanjing Huang


Abstract
In this work, we explore the way to quickly adjust an existing named entity recognition (NER) system to make it capable of recognizing entity types not defined in the system. As an illustrative example, consider the case that a NER system has been built to recognize person and organization names, and now it requires to additionally recognize job titles. Such a situation is common in the industrial areas, where the entity types required to recognize vary a lot in different products and keep changing. To avoid laborious data labeling and achieve fast adaptation, we propose to adjust the existing NER system using the previously labeled data and entity lexicons of the newly introduced entity types. We formulate such a task as a partially supervised learning problem and accordingly propose an effective algorithm to solve the problem. Comprehensive experimental studies on several public NER datasets validate the effectiveness of our method.
Anthology ID:
2020.findings-emnlp.60
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:
678–688
Language:
URL:
https://www.aclweb.org/anthology/2020.findings-emnlp.60
DOI:
10.18653/v1/2020.findings-emnlp.60
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.findings-emnlp.60.pdf