Abstract
Chinese part-of-speech tagging (POS tagging) is a key part of Chinese Natural Language Processing (CNLP) Research. Using POS tagging can better understand semantics and improve the efficiency of Natural Language Processing. This paper proposes a method of POS tagging based on a bidirectional GRU and CRF hybrid model, which can automatically learn features and reduce operational complexity. Under the same conditions, compared with Bi-LSTM+CRF, CNN+LSTM, LSTM+CRF and HMM, the model obtains the best accuracy.
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Acknowledgments
This work is funded by Public Welfare Technology Application Projects of Zhejiang Province under Grant (2016C31072), Research Project of Educational Reform in Zhejiang (jg20160053), Zhejiang Natural Science Foundation (LQ17E050013), Education Department-Autodesk Inc specialized comprehensive reform Project (No. joz201401), State Scholarship Fund.
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Guo, Jj., Wang, Sp., Yu, Ch., Song, Jy. (2019). Chinese POS Tagging Method Based on Bi-GRU+CRF Hybrid Model. In: Xhafa, F., Barolli, L., Greguš, M. (eds) Advances in Intelligent Networking and Collaborative Systems. INCoS 2018. Lecture Notes on Data Engineering and Communications Technologies, vol 23. Springer, Cham. https://doi.org/10.1007/978-3-319-98557-2_41
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DOI: https://doi.org/10.1007/978-3-319-98557-2_41
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