Journal on Communications ›› 2018, Vol. 39 ›› Issue (7): 103-112.doi: 10.11959/j.issn.1000-436x.2018118

• Papers • Previous Articles     Next Articles

Multi-factor person entity relation extraction model based on distant supervision

Yangchen HUANG1(),Yan JIA1,Liang GAN1,Jing XU1,Jiuming HUANG1,Zhonghe HE2   

  1. 1 College of Computer,National University of Defense Technology,Changsha 410073,China
    2 KB R&D department,Hunan Singhand Intelligent Data Technology Co.,Ltd.,Changsha 410205,China
  • Revised:2018-06-21 Online:2018-07-01 Published:2018-08-08
  • Supported by:
    The National Key Research and Development Program of China(2016QY03D0601);The National Key Research and Development Program of China(2016QY03D0603);The National Natural Science Foundation of China(61502517);Key Research and Development Plan of Hunan Province(2018GK2056)


Aiming at the problem that the basic assumption of distant supervision was too strong and easy to produce noise data,a model of the person entity relation extraction which could automatically filter the training data generated by distant supervision was proposed.For training data generation,the data produced by distant supervision would be filtered by multiple instance learning and the method of TF-IDF-based relation keyword detecting,which tried to make the training data has the manual annotation quality.Furthermore,the model combined lexical and syntactic features to extract the effective relation feature vector from two angles of words and semantics for classifier.The experiment results on large scale real-world datasets show that the proposed model outperforms other relation extraction methods which based on distant supervision.

Key words: relation extraction, person entity relation, distant supervision, machine learning, natural language processing

CLC Number: 

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