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首页> 外文会议>International Congress on Image and Signal Processing, BioMedical Engineering and Informatics >Ship Fault Named Entity Recognition Based on Bilayer Bi-LSTM-CRF
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Ship Fault Named Entity Recognition Based on Bilayer Bi-LSTM-CRF

机译：基于双层Bi-LSTM-CRF的舰船故障命名实体识别

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摘要

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.
机译：在中国电子舰船故障的命名实体识别任务中，传统的命名实体识别方法高度依赖于人工特征提取。因此，本文结合条件随机场（CRF）网络模型设计了双向长短期记忆（Bi-LSTM）网络，以优化舰船故障命名实体识别的准确性。首先，对中国舰船故障数据集进行脱敏，并对脱敏后的文本序列进行预处理。其次，结合词嵌入技术，利用双向长短期（Bi-LSTM）网络模型，将船舶故障的文本序列映射到低维向量空间，构造前后语义特征。最后，进入条件随机场（CRF）层后对数据的输入和输出进行分析，通过条件随机场（CRF）层获得整个文本序列的最优标签，并在此基础上提取实体。实验结果表明，将双向双向长短期记忆（Bi-LSTM）网络与条件随机场（CRF）相结合的模型方法可以有效提高中国舰船故障命名实体识别的准确性。

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