With the rapid development in AI technology, constructing the nationally independent intelligent railway is a trend at present in Chinese rail transit industry. As CTCS-3 is one of the core technologies for Chinese high-speed railway, making machines understand the CTCS-3 knowledge efficiently and concretely is becoming an important topic. Knowledge extraction is one of the most significant parts. Therefore, we proposed a method to extract CTCS-3 knowledge from unstructured data by combining BERT and BiLSTM-CRF. We built a 407936-word labeled dataset about CTCS-3 equipment for model training. Using such small dataset, we completed the experiments of CTCS-3 knowledge extraction including entity recognition and relationship extraction. The experiment show that the F1 score is 75.89%. By the method, we can get some entity relationship triples which are the foundation to achieve cognitive intelligence of CTCS-3. In summary, it extracts CTCS-3 entity relation triples with a small number of rail transit industry dataset.


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    Title :

    A Method for CTCS-3 Knowledge Extraction of Unstructured Data


    Additional title:

    Lect. Notes Electrical Eng.


    Contributors:
    Liang, Jianying (editor) / Jia, Limin (editor) / Qin, Yong (editor) / Liu, Zhigang (editor) / Diao, Lijun (editor) / An, Min (editor) / Wu, Hao (author) / Li, Saifei (author) / Li, Hua (author) / Wang, Yu (author)

    Conference:

    International Conference on Electrical and Information Technologies for Rail Transportation ; 2021 October 21, 2021 - October 23, 2021



    Publication date :

    2022-02-19


    Size :

    8 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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