嵌套命名实体识别研究进展

doi:10.11896/jsjkx.201100165

摘要/Abstract

摘要： 嵌套命名实体之间蕴含着丰富的语义关系与结构信息,对于关系抽取、事件抽取等下游任务的执行至关重要。近年来,深度学习技术由于能够获取文本中更为丰富的表征信息,在文本信息抽取模型的精确度上已经逐渐超过了传统基于规则的方法,因此许多学者开展了基于深度学习的嵌套命名实体识别技术研究,并获得了目前最先进的性能。对现有的嵌套命名实体识别技术进行了全面的综述,介绍了嵌套命名实体识别最具代表性的方法及最新应用技术,并对未来面临的挑战和发展方向进行了探讨和展望。

关键词: 超图, 跨度, 命名实体识别, 嵌套命名实体, 序列标注

Abstract: There are rich semantic relations and structural information between nested named entities,which is very important for the implementation of downstream tasks such as relation extraction and event extraction.The accuracy of text information extraction model has gradually exceeded the traditional rule-based method.Therefore,many scholars have carried out research on nested named entity recognition technology based on deep learning,and obtained the most advanced performance.This paper reviews the existing nested named entity recognition technology,This paper gives a comprehensive review of the existing nested named entity recognition technology,introduces the most representative methods and the latest application technology of nested named entity recognition,and discusses and prospects the challenges and development direction in the future.

Key words: Hypergraph, Named entity recognition, Nested named entities, Sequence labeling, Span

中图分类号:

TP312

余诗媛, 郭淑明, 黄瑞阳, 张建朋, 苏珂. 嵌套命名实体识别研究进展[J]. 计算机科学, 2021, 48(11A): 1-10. https://doi.org/10.11896/jsjkx.201100165

YU Shi-yuan, GUO Shu-ming, HUANG Rui-yang, ZHANG Jian-peng, SU Ke. Overview of Nested Named Entity Recognition[J]. Computer Science, 2021, 48(11A): 1-10. https://doi.org/10.11896/jsjkx.201100165

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