Computer Science ›› 2018, Vol. 45 ›› Issue (4): 66-70.doi: 10.11896/j.issn.1002-137X.2018.04.009

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Chinese Part-of-speech Tagging Model Using Attention-based LSTM

SI Nian-wen, WANG Heng-jun, LI Wei, SHAN Yi-dong and XIE Peng-cheng   

  • Online:2018-04-15 Published:2018-05-11

Abstract: Because traditional statistical model based Chinese part-of-speech tagging relies heavily on manually designed features,this paper proposed an effective attention based long short-term memory model for Chinese part-of-speech tagging.The proposed model utilizes the basic distributed word vector as the unit input,and extracts rich contextual feature representation with bidirectional long short-term memory.At the same time,an attention based hidden layer is added in the network,and the attention probability is distributed for hidden state in different time to optimize and improve the quality of hidden vector.The state transition probability is employed in decoding process to further improve accuracy.Experimental results on PKU and CTB5 dataset show that the proposed model is able to make Chinese part-of-speech tagging effectively.It achieves higher accuracy than traditional methods and gets competitive results compared with state-of-the-art models.

Key words: Part-of-speech tagging,Long short-term memory,Attention mechanism,Contextual feature

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