@inproceedings{peng-etal-2020-toward,
title = "Toward Recognizing More Entity Types in {NER}: An Efficient Implementation using Only Entity Lexicons",
author = "Peng, Minlong and
Ma, Ruotian and
Zhang, Qi and
Zhao, Lujun and
Wei, Mengxi and
Sun, Changlong and
Huang, Xuanjing",
editor = "Cohn, Trevor and
He, Yulan and
Liu, Yang",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.findings-emnlp.60",
doi = "10.18653/v1/2020.findings-emnlp.60",
pages = "678--688",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Toward Recognizing More Entity Types in NER: An Efficient Implementation using Only Entity Lexicons
%A Peng, Minlong
%A Ma, Ruotian
%A Zhang, Qi
%A Zhao, Lujun
%A Wei, Mengxi
%A Sun, Changlong
%A Huang, Xuanjing
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Findings of the Association for Computational Linguistics: EMNLP 2020
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F peng-etal-2020-toward
%X 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.
%R 10.18653/v1/2020.findings-emnlp.60
%U https://aclanthology.org/2020.findings-emnlp.60
%U https://doi.org/10.18653/v1/2020.findings-emnlp.60
%P 678-688
Markdown (Informal)
[Toward Recognizing More Entity Types in NER: An Efficient Implementation using Only Entity Lexicons](https://aclanthology.org/2020.findings-emnlp.60) (Peng et al., Findings 2020)
ACL