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Journal of System Simulation

Abstract

Abstract: Compared with the text recognition in conventional fields, it is difficult to recognize the large number of nested named entities in professional terms. This is also one of the care challenges in building the knowledge graph in aerospace field. For the named entity recognition technologies, bidirectional long short-term memory network plus conditional random field (BiLSTM-CRF) is often used to identify entities, which is difficult to distinguish the complex relationships such as nesting and intersection of terms in missile field. In order to solve the problem, based on the nested entity labeling of domain text, a nested named entity recognition method based on linguistic features and machine reading comprehension is proposed, in which prior knowledge is introduced, decoding method is changed, and multi-task predictions are carried out in the form of question and answer. Experiments show that the proposed method can greatly improve the accuracy and recall rate of text nested entity recognition in missile field, in which the comprehensive index F1 value is improved by 13.89% compared with the nested named entity recognition method based on BiLSTM-CRF.

First Page

1757

Last Page

1767

CLC

TP391.1

Recommended Citation

Guan Jingwen, Song Xiao, Li Xiaoqing, et al. Research on Nested Named Entity Recognition in Missile Field Text[J]. Journal of System Simulation, 2023, 35(8): 1757-1767.

Corresponding Author

Song Xiao

DOI

10.16182/j.issn1004731x.joss.22-0456

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