Abstract
In this paper, we present a neural architecture for recognizing named entity in text railway accident and fault analysis report, this neural architecture incorporates word representation and Conditional Random Field (CRF) into Bidirection Long Short-Term Memory (BiLSTM) neural network. We used Google's latest open source TensorFlow software platform to build this BiLSTM-CRF deep learning model. Based on the experiment analysis of railway accident and fault text data from 2016 to 2017, the result shows that the model has a significant improvement in recognizing named entity accuracy and recall rate, then we applyed the training model to our engineering application system to automatic extraction entity. The practice proves that the model has good applicability in theory and practice, meanwhile this experiment lay the foundation for much more text analysis in railway field.
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