Computer Science ›› 2022, Vol. 49 ›› Issue (5): 200-205.doi: 10.11896/jsjkx.210300198

• Artificial Intelligence • Previous Articles     Next Articles

Dialogue-based Entity Relation Extraction with Knowledge

LU Liang, KONG Fang   

  1. School of Computer Science and Technology,Soochow University,Suzhou,Jiangsu 215006,China
  • Received:2021-03-19 Revised:2021-07-16 Online:2022-05-15 Published:2022-05-06
  • About author:LU Liang,born in 1995,postgraduate,is a member of China Computer Federation.His main research interests include natural language processing and so on.
    KONG Fang,born in 1977,Ph.D,professor,Ph.D supervisor,is a member of China Computer Federation.Her main research interests include natural language processing and discourse ana-lysis.
  • Supported by:
    General Program of National Natural Science Foundation of China(61876118) and Key Program of National Natural Foundation of China(61836007).

Abstract: Entity relation extraction aims to extract semantic relations between entities from text.Up to now,related work on entity relation extraction mainly focuses on written texts,such as news and Wikipedia text,and has achieved considerable success.However,the research for dialogue texts is still in initial stage.At present,the dialogue corpus used for entity relation extraction is small in scale and low in information density,so it is difficult to capture effective features.The deep learning model does not associate knowledge like human beings,so it is difficult to understand the dialogue content in detail and depth simply by increasing the amount of annotation data and enhancing the computing power.In response to the above problems,this paper proposes a knowledge-integrated entity relation extraction model,which uses Star-Transformer to effectively capture features from dialogue texts,and constructs a relation set containing relations and their semantic keywords through the co-occurrence of keywords.The important relation features obtained by calculating the correlation between the set and dialogue text are integrated into the model as knowledge.Experiment results on the DialogRE dataset show that the F1 value is 53.6% and the F1c value is 49.5%,which proves the effectiveness of proposed method.

Key words: Dialogue context, Entity relation extraction, Transformer, Attention mechanism, Integrate knowledge

CLC Number: 

  • TP391
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