基于分层标注的中文嵌套命名实体识别

doi:10.12066/j.issn.1007-2861.2283

Abstract

Abstract:

Chinese named entity recognition plays a critical role in Chinese information processing. In Chinese information text, many named entities contain nested entities. However, most recent studies have focused solely on the recognition of flat entities, which cannot fully capture the boundary information between nested entities. In this study, a hierarchical tagging method is used for nested named entity recognition (NNER), in which each layer of entity recognition is parsed into a separate task, and a gated filtering mechanism is used to promote information exchange between layers. Experiments are conducted on the public NNER corpus of the People's Daily from 1998 to verify the effectiveness of the model. Experimental results show that the F1 value of this method on the People's Daily dataset reach 91.41% without using external resource dictionary information. Thus, the method is shown to improve the recognition of Chinese nested named entities.

Key words: Chinese information processing, hierarchical tagging, nested named entity recognition (NNER), gated filtering mechanism

CLC Number:

TP391.1

JIN Yanliang, XIE Jinfei, WU Dijia. Chinese nested named entity recognition based on hierarchical tagging[J]. Journal of Shanghai University（Natural Science Edition）, 2022, 28(2): 270-280.

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Chinese nested named entity recognition based on hierarchical tagging

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