命名实体识别任务综述

doi:10.11896/jsjkx.220200119

Abstract

Abstract: Named entity recognition,as a very basic task in natural language processing,lays the foundation for the efficient completion of many other downstream tasks.Its purpose is to identify the corresponding entity from a text described in natural language and label its type,so as to make preparations for data labeling for other related tasks.This paper first introduces the deve-lopment process of named entity recognition tasks and the key methods used in related research in the corresponding context,including the rule-based and dictionary-based methods used in the early days of the birth,and the statistics and deep learning derived from the later development.Secondly,it summarizes some of the more mainstream research directions in this field,including named entity recognition under low-resource conditions,nested named entity recognition,and cross-language named entity recognition.These directions are the hot research trends of this task recently,including the most popular research method of this task at present.Finally,the relevant experience in the research is summarized,and the future development direction and difficulties of the task are prospected.

Key words: Named entity recognition, Nested named entity recognition, Deep learning, Low-resource, Cross-language

CLC Number:

TP391

GAO Xiang, WANG Shi, ZHU Junwu, LIANG Mingxuan, LI Yang, JIAO Zhixiang. Overview of Named Entity Recognition Tasks[J].Computer Science, 2023, 50(6A): 220200119-8.

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