Computer Science ›› 2017, Vol. 44 ›› Issue (10): 249-253.doi: 10.11896/j.issn.1002-137X.2017.10.045

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Chinese Medical Weak Supervised Relation Extraction Based on Convolution Neural Network

LIU Kai, FU Hai-dong, ZOU Yu-wei and GU Jin-guang   

  • Online:2018-12-01 Published:2018-12-01

Abstract: With medical field are receiving more and more attention,the theory and application of natural language processing began to expand the field,and information extraction technology in the field of application has become a research hotspot.In this paper,based on the application of information extraction technology in medical domain entity relation extraction,a weak supervised relation extraction method based on convolution neural network was proposed.This me-thod adds the artificial rules to the training corpus with the entity relation label,and then transforms the weak relation training corpus into the vector characteristic matrix,next inputs it into the convolution neural network for training the classification model,and finally realizes the entity relation extraction.The experimental results show that the method is more accurate and efficient than the conventional machine learning method.

Key words: Natural language processing,Entity relation extraction,Weak supervision,Convolutional neural network

[1] XU J,ZHANG Z X,WU Z X.Review on Techniques of Entity Relation Extraction[J].New Technology of Library and Information Service,2008(8):1003-3513.(in Chinese) 徐健,张智雄,吴振新.实体关系抽取的技术方法综述[J].现代图书情报技术,2008(8):1003-3513.
[2] YAO L M,RIEDEL S,MCCALLUM A.Unsupervised relation discovery with sense disambiguation[C]∥Proceedings of ACL 2012.2012:712-720.
[3] FADER A,SODERLAND S,ETZIONI O.Identifying relations for open information extraction [C]∥Proceedings of EMNLP 2011.2011:1535-1545.
[4] GRISHMAN R.Information Extraction[M]∥Information Retrieval.Springer New York,2003:8-15.
[5] KAMBHATLA N.Combining lexical,syntactic,and semanticfeatures with maximum entropy models for extracting relations[C]∥ACL 2004 on Interactive Poster and Demonstration Sessions.Association for Computational Linguistics,2004:22.
[6] ZHOU G D,SU J,ZHANG J,et al.Exploring various know-ledge in relation extraction[C]∥ Proceedings of ACL 2005.2005:427-434.
[7] JIANG J,ZHAI C X.A systematic exploration of the feature space for relation extraction[C]∥ Proceedings of HLT-NAACL 2007.2007:113-120.
[8] ZELENKO D,AONE C,RICHARADELLA A.Kernel methods for relation extraction[J].The Journal of Machine Learning Research,2003,3(3):1083-1106.
[9] ZHANG M,ZHANG J,SU J,et al.Acomposite kernel to extract relation sbetween entities with both flat and structured features[C]∥ Proceedings of ACL 2006.2006:825-832.
[10] ZHOU G D,ZHANG M,JI D H,et al.Tree kernel-based relation extraction with context-sensitive structured parse tree information[C]∥Proceedings of EMNLP CoNLL 2007.2007:728-736.
[11] CRAVEN M,KUMLIEN J.Constructing biological knowledge bases by extracting information from text sources[C]∥Procee-dings of AAAI 1999.1999:77-86.
[12] RIEDEL S,YAO L M,MCCALLUM A.Modeling relations and their mentions without labeled text[C]∥Procedings of ECML PKDD 2010.2010:148-163.
[13] SURDEANU M,TIBSHIRANI J,N ALLAPATI R,et al.Manning.Multi-instance multi-label learning for relation extraction[C]∥Proceedings of EMNLP-CoNLL 2012.2012:455-465.
[14] XU W,HOFFMANN R,ZHAO L,et al.Filling knowledge base gaps for distant supervision of relation extraction[C]∥Procee-dings of ACL 2013.2013:665-670.
[15] CHEN Y,GENG G H,JIA H.Density center graph basedweakly supervised classification algorithm[J].Computer Engineering and Applications,2015,51(6):6-10.(in Chinese) 陈燕,耿国华,贾晖.基于密度中心图的弱监督分类方法[J].计算机工程与应用,2015,51(6):6-10.
[16] COLLOBERT R,WESTON J,BOTTOU L,et al.Natural language processing (almost) from scratch[J].The Journal of Machine Learning Research,2011,12(1):2493-2537.
[17] YIH W,HE X,Meek C.Semantic Parsing for Single- Relation Question Answering[C]∥Proceedings of ACL 2014.2014:643-648.
[18] KIM Y.Convolutional neural networks for sentence classification[J].Eprint Arxiv,2014:1408-5882.
[19] ZENG D,LIU K,LAI S,et al.Relation Classification via Convolutional Deep Neural Network[C]∥Proceedings of COLING 2014.2014:2335-2344.
[20] ZOU Y W,GU J G,FU H D.EARES:Medical Entity and Attribute Extraction System based on Relation Annotation[J].Wuhan University Journal of Natural Sciences,2016,2(21):145-150.
[21] ZENG D J.Research on Key Technologies of Relational Extraction for Unstructured Text[D].Beijing:University of Chinese Academy of Sciences,2015.(in Chinese) 曾道建.面向非结构化文本的关系抽取关键技术研究[D].北京:中国科学院自动化研究所,2015.

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