Computer Science ›› 2020, Vol. 47 ›› Issue (6A): 40-44.doi: 10.11896/JsJkx.190700042

• Artificial Intelligence • Previous Articles     Next Articles

Relation Extraction Method Combining Encyclopedia Knowledge and Sentence Semantic Features

YU Yi-lin, TIAN Hong-tao, GAO Jian-wei and WAN Huai-yu   

  1. School of Computer and Information Technology,BeiJing Jiaotong University,BeiJing 100044,China
  • Published:2020-07-07
  • About author:LYU Yi-lin, born in 1997, undergra-duate student.His main research interest is information extraction.
    WAN Huai-yu, born in 1981, Ph.D, associate professor, Ph.D supervisor, is a member of China Computer Federation.His main research interests include social network mining, text mining, user behavior analysis, and traffic data mining.
  • Supported by:
    This work was supported by the National Key R&D Program of China (2018YFC0830200).

Abstract: Relation extraction is one of the important research topics in the field of information extraction.Its typical application scenarios include knowledge graphs,question answering systems,machine translation,etc.Recently,deep learning has been applied in a large amount of relation extraction researches,and deep neural networks based relationship extraction method performs much better than the traditional methods in many situations.However,most of the current deep neural network-based relation extraction methods Just rely on the corpus itself and lack the introduction of external knowledge.To address this issue,this paper proposed a neural network model,which combined encyclopedia knowledge and semantic features of sentences for relation extraction.The model introduced the description information of entities in encyclopedia as external knowledge,and dynamically extracted entity features through attention mechanism.Meanwhile,it employed bidirectional LSTM networks to extract the semantic features contained in the sentence.Finally,the model combined the entity features and the sentence semantic features for relation extraction.A series of experiments were carried out based on a manually labeled dataset.Experimental results demonstrate that the proposed model is superior to other existing relationship extraction methods.

Key words: Attention mechanism, Chinese encyclopedia, Entity relation extraction, Long short-term memory

CLC Number: 

  • TP391
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