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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    Titel :

    A Method for CTCS-3 Knowledge Extraction of Unstructured Data


    Weitere Titelangaben:

    Lect. Notes Electrical Eng.


    Beteiligte:
    Liang, Jianying (Herausgeber:in) / Jia, Limin (Herausgeber:in) / Qin, Yong (Herausgeber:in) / Liu, Zhigang (Herausgeber:in) / Diao, Lijun (Herausgeber:in) / An, Min (Herausgeber:in) / Wu, Hao (Autor:in) / Li, Saifei (Autor:in) / Li, Hua (Autor:in) / Wang, Yu (Autor:in)

    Kongress:

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



    Erscheinungsdatum :

    2022-02-19


    Format / Umfang :

    8 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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