Ship Fault Named Entity Recognition Based on Bilayer Bi-LSTM-CRF

In the named entity recognition task of Chinese electronic ship failure, traditional named entity recognition methods highly rely on manual feature extraction. Therefore, this paper designs a bidirectional long short-term memory (Bi-LSTM) network combined with conditional random field (CRF) network model to optimize the accuracy of ship fault named entity recognition. Firstly, the Chinese ship fault data set is desensitized, and the desensitized text sequence is preprocessed; secondly, the text sequence of ship fault is mapped to the low dimensional vector space by combining the word embedding technology, using the bidirectional long short-term (Bi-LSTM) network model to construct forward and backward semantic features; finally, the input and output of the data are analyzed after entering the conditional random field (CRF) layer, the optimal label of the whole text sequence is obtained through the conditional random field (CRF) layer, and the entity is extracted on this basis. The experimental results show that the model method of combining bilayer bidirectional long short-term memory (Bi-LSTM) network and conditional random field (CRF) can effectively improve the accuracy of named entity recognition of Chinese ship fault.