Computer Science

Computer Science ›› 2023, Vol. 50 ›› Issue (11A): 221100065-6.doi: 10.11896/jsjkx.221100065

• Artificial Intelligence • Previous Articles     Next Articles

Relation Recognition of Unmarked Complex Sentences Based on Feature Fusion

YANG Jincai1, MA Chen1, XIAO Ming2   

  1. 1 School of Computer Science,Central China Normal University,Wuhan 430079,China
    2 Research Center for Language and Language Education,Central China Normal University,Wuhan 430079,China
  • Published:2023-11-09
  • About author:YANG Jincai,born in 1976,doctor,professor,doctoral supervisor,is a member of China Computer Federation.His main research interests include advanced database and information system,Chinese information processing,artificial intelligence and natural language processing.
  • Supported by:
    National Social Science Fund of China(19BYY092).

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Abstract: Unlike marked complex sentences,which lack the assistance of relation words,the identification of unmarked complex sentences is a difficult task in natural language processing.Integrating part of speech features into word vectors,and the word vector representation containing external features is obtained by training.By combining the BERT model and the BiLSTM model,the word vector and the part-of-speech vector are combined for training,and the polar feature information captured by BiLSTM model and the dependency syntax feature information captured by CNN model are added to the feature fusion layer.Experimental results show that the methods of adding features and combining multiple deep learning models can achieve better results in classification of Chinese complex sentences.Compared with the benchmark model,the macro F1 value and micro F1 value are improved.The best classification effect achieves 83.67% micro F1 value in the top layer classification and 68.28% micro F1 value in the second layer classification.

Key words: Unmarked complex sentence, BERT, Feature fusion, Deep learning

CLC Number: 

  • TP391
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