Computer Science

Computer Science ›› 2021, Vol. 48 ›› Issue (6A): 295-298.doi: 10.11896/jsjkx.200500019

• Intelligent Computing • Previous Articles     Next Articles

Relation Classification of Chinese Causal Compound Sentences Based on Transformer Model and Relational Word Feature

YANG Jin-cai, CAO Yuan, HU Quan, SHEN Xian-jun   

  1. School of Computer Science,Central China Normal University,Wuhan 430079,China
  • Online:2021-06-10 Published:2021-06-17
  • About author:YANG Jin-cai,born in 1967,Ph.D,professor,Ph.D 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 Fundation of China(19BYY092).

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Abstract: Chinese compound sentences have rich and complicated semantic relations.Recognition of relation category of a Chinese compound sentence is judgment of semantic relations of the sentence,and it is very important to analyze the meaning of compound sentences.Causal compound sentences are the most frequently used sentences in Chinese article.In this paper,a corpus of causal compound sentences with two clauses is taken as research object.A deep learning method is used in order to mine the hidden features of compound sentences automatically.At the same time,this paperintegrates significant linguistics knowledge of relational words in the proposed model.Combining word vector of word2vec and relation word feature of one hot coding as input to the model,a transformer model using convolutional neural network as feedforward layer is exploited to identify the relation category of causal compound sentences.Using our model,the F1 value of the experiment reaches 92.13%,which is better than the compa-rative model.

Key words: Causal compound sentences, Deep learning, Relation classification, Transformer model, Word vector

CLC Number: 

  • TP391
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