Citation

Zhang P. China Saf. Sci. J. 2022; 32(6): 109-114.

Copyright

(Copyright © 2022, China Occupational Safety and Health Association, Publisher Gai Xue bao)

DOI

10.16265/j.cnki.issn1003-3033.2022.06.2732

PMID

unavailable

Abstract

In order to address difficulty in data analysis in investigation reports of railway equipment accidents, an accident information extraction method based on multi-dimensional character feature representation was proposed. Firstly, a subject pattern matching method was put forward for data preprocessing stage to extract subject paragraphs to which named entity belonged. For text feature representation, a multi-dimensional feature representation method was proposed to transform text into feature vector, and training of named entity recognition model was carried out by using bidirection long short term memory (BiLSTM) + conditional random fields (CRF) neural network. Finally, accident investigation report was used for experimental verification. The results show that the comprehensive evaluation index of multi-dimensional character +BiLSTM+CRF model is improved by 22. 86% through preprocessing of subject pattern matching. And compared with word2vec feature representation, multi- dimensional one can improve evaluation index of BiLSTM+CRF model by 4. 89%. © 2022 China Safety Science Journal. All rights reserved.

Language: zh