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
- Editors:
- Trevor Cohn, Yulan He, Yang Liu
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 678–688
- Language:
- URL:
- https://aclanthology.org/2020.findings-emnlp.60
- DOI:
- 10.18653/v1/2020.findings-emnlp.60
- Cite (ACL):
- Minlong Peng, Ruotian Ma, Qi Zhang, Lujun Zhao, Mengxi Wei, Changlong Sun, and Xuanjing Huang. 2020. Toward Recognizing More Entity Types in NER: An Efficient Implementation using Only Entity Lexicons. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 678–688, Online. Association for Computational Linguistics.
- Cite (Informal):
- Toward Recognizing More Entity Types in NER: An Efficient Implementation using Only Entity Lexicons (Peng et al., Findings 2020)
- PDF:
- https://preview.aclanthology.org/naacl24-info/2020.findings-emnlp.60.pdf
- Data
- CoNLL 2003