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%.

Key words: multi-dimensional character feature, railway equipment accident, information extraction, subject pattern matching, named entity recognition.