A multi-strategy fusion neural network method for named entity recognition in PST

Yong Kang Zhu; Pei Yan Wang; Rui Ting Li; Dong Feng Cai

doi:10.1117/12.3014744

4 March 2024 A multi-strategy fusion neural network method for named entity recognition in PST

Yong Kang Zhu, Pei Yan Wang, Rui Ting Li, Dong Feng Cai

Proceedings Volume 12981, Ninth International Symposium on Sensors, Mechatronics, and Automation System (ISSMAS 2023); 129811R (2024) https://doi.org/10.1117/12.3014744
Event: 9th International Symposium on Sensors, Mechatronics, and Automation (ISSMAS 2023), 2023, Nanjing, China

Abstract

This paper presents a novel multi-strategy fusion neural network Named Entity Recognition (NER) method aimed at identifying entities in Process Specification Text (PST). To address the challenges of short sentence length, entity type error, and entity boundary error caused by limited context and semantic information in small sample data, the proposed method incorporates semantic enhancement (BM25, SBERT) and data augmentation (AUG) techniques. Additionally, a constraint matrix (Matrix) is designed to improve the consistency of the model and address the inconsistency caused by the pre-training model in the BERT-CRF model. The proposed method demonstrates significant improvements in both model consistency and F1 value. Specifically, the model consistency is improved from 90.71% to 94.51%, and the F1 value is increased from 76.90% to 77.21%. These results validate the effectiveness of the proposed approach for NER in PST.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yong Kang Zhu, Pei Yan Wang, Rui Ting Li, and Dong Feng Cai "A multi-strategy fusion neural network method for named entity recognition in PST", Proc. SPIE 12981, Ninth International Symposium on Sensors, Mechatronics, and Automation System (ISSMAS 2023), 129811R (4 March 2024); https://doi.org/10.1117/12.3014744

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