计算机科学 ›› 2021, Vol. 48 ›› Issue (10): 77-84.doi: 10.11896/jsjkx.210300271
张世豪, 杜圣东, 贾真, 李天瑞
ZHANG Shi-hao, DU Sheng-dong, JIA Zhen, LI Tian-rui
摘要: 随着医学信息化的推进,医学领域已经积累了海量的非结构化文本数据,如何从这些医学文本中挖掘出有价值的信息,是医学行业和自然语言处理领域的研究热点。随着深度学习的发展,深度神经网络被逐步应用到关系抽取任务中,其中“recurrent+CNN”网络框架成为了医学实体关系抽取任务中的主流模型。但由于医学文本存在实体分布密度较高、实体之间的关系交错互联等问题,使得 “recurrent+CNN”网络框架无法深入挖掘医学文本语句的语义特征。基于此,在“recurrent+CNN”网络框架基础之上,提出一种融合多通道自注意力机制的中文医学实体关系抽取模型,包括:1)利用BLSTM捕获文本句子的上下文信息;2)利用多通道自注意力机制深入挖掘句子的全局语义特征;3)利用CNN捕获句子的局部短语特征。通过在中文医学文本数据集上进行实验,验证了该模型的有效性,其精确率、召回率和F1值与主流的模型相比均有提高。
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[1]GOLSHAN P N,DASHTI H R,AZIZI S,et al.A Study of Recent Contributions on Information Extraction[J].arXiv:1803.05667,2018. [2]LIU Q,LI Y,DUAN H,et al.Knowledge Graph Construction Techniques[J].Journal of Computer Research and Development,2016,53:582-600. [3]GRISHMAN R,SUNDHEIM B.Message understanding confe-rence-6:a brief history[C]//Proceedings of the 16th Conference on Computational Linguistics.New York:ACM Press,1996:466-471. [4]UZUNER O,SOUTH B,SHEN S Y,et al.2010 i2b2/VA challenge on concepts,assertions,and relations in clinical text[J].Journal of the American Medical Informatics Association,2011,18(5):552-556. [5]NING S M,TENG F,LI T R.Multi-Channel Self-AttentionMechanism for Relation Extraction in Clinical Records[J].Chinese Journal of Computers,2020,43(5):916-929. [6]HAN X,GAO T Y,LIN Y K,et al.More data,more relations,more context and more openness:a Review and outlook for relation extraction[J].arXiv:2004.03186,2020. [7]ZHAO S,GRISHMAN R.Extraction relations with integrated information using kernel methods[C]//Proceedings of the 41st Annual Meeting of the Association for Computational Linguistics.Stroudsburg:ACL,2005:419-426. [8]GUO X Y,HE T T,HU X H,et al.Chinese Named Entity Relation Extraction Based Syntactic and Semantic Features[J].Journal of Chinese Information Processing,2014,28(6):183-189. [9]KAMBHATLA N.Combining lexical,syntactic,and semanticfeatures with maximum entropy models for extracting relation[C]//Proceedings of ACL on Interactive Poster and Demonstration Sessions.Stroudsburg:ACL,2004:22-26. [10]ZHOU J.Chinese entity relation extraction based on conditional random fields model[J].Computer Engineering,2010,36(24):192-194. [11]RINK B,HARABAGIU S,ROBERTS K.Automatic extraction of relations between medical concepts in clinical texts[J].Journal of the American Medical Informatics Association,2011,18(5):594-600. [12]D'SOUZA J,NG V.Ensemble-Based Medical Relation Classification[C]//25th International Conference on Computational Linguistics.Dublin:COLING,2014:1682-1693. [13]KIM S,LIU H,YEGANOVA L,et al.Extracting drug-drug interactions from literature using a rich feature-based linear kernel approach[J].Journal of Biomedical Informatics,2015,55(2):23-30. [14]ZENG D J,LIU K,LAI S W,et al.Relation classification via convolutional deep neural network[C]//Proceedings of the 25th International Conference on Computational Linguistics.Stroudsburg:ACL,2014:2335-2344. [15]ZHANG D X,WANG D.Relation classification via recurrent neural network[J].arXiv:1508.01006,2015. [16]ZHANG S,ZHENG D Q,HU X C,et al.Bidirectional Long short-term memory networks for relation classification[C]//Proceedings of the 29th Pacific Asia Conference on Language,Information and Computation.Stroudsburg:ACL,2015:73-78. [17]ZHU J Z,QIAO J Z,DAI X X,et al.Relation classification via target-concentrated attention CNNs[C]//International Confe-rence on Neural Information Processing.Berlin:Springer,2017:137-146. [18]ZHOU P,SHI W,TIAN J,et al.Attention-based bidirectional long short-term memory networks for relation classification[C]//Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics.Stroudsburg:ACL,2016:207-212. [19]LEE J,SEO S,CHOI Y S.Semantic relation classification via bidirectional LSTM networks with entity-aware attention using latent entity typing[J].arXiv:1901.08163,2019. [20]CAI R,ZHANG X D,WANG H F.Bidirectional recurrent con-volutional neural network for relation classification[C]//Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics.Stroudsburg:ACL,2016:756-765. [21]ZHANG X B,CHEN F C,HUANG R Y.A combination of RNN and CNN for attention-based relation classification[J].Procedia Computer Science,2018,131:911-917. [22]TRAN V H,PHI V T,SHINDO H,et al.Relation Classification Using Segment-Level Attention-based CNN and Dependency-based RNN[C]//Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics.Stroudsburg:ACL,2019:2793-2798. [23]SAHU S,ANAND A,ORUGANTY K,et al.Relation extraction from clinical texts using domain invariant convolutional neural network[C]//Proceedings of the 15th Workshop on Biomedical Natural Language Processing.2016:206-215. [24]ZHOU H W,LANG C K,LIU Z,et al.Knowledge-guided con-volutional networks for chemical-disease relation extraction[J].BMC Bioinformatics,2019,20(1):260-273. [25]SAHU S,ANAND A.Drug-Drug Interaction Extraction from Biomedical Texts Using Long Short-Term Memory Network[J].Journal of Biomedical Informatics,2018,86:15-24. [26]BAI T,WANG C,WANG Y,et al.A novel deep learning me-thod for extracting unspecific biomedical relation[J].Concurrency and Computation:Practice and Experience,2020,32:1-11. [27]RAJ D,SAHU S,ANAND A.Learning local and global contexts using a convolutional recurrent network model for relation classification in biomedical text[C]//Proceedings of the 21st Conference on Computational Natural Language Learning.Vancouver:CoNLL,2017:311-321. [28]HE B,GUAN Y,DAI R.Convolutional Gated Recurrent Units for Medical Relation Classification[C]//2018 IEEE InternationalConference on Bioinformatics and Biomedicine.New York:IEEE Press,2019:646-650. [29]LIN Z,FENG M,SANTOS C N,et al.A structured self-attentive sentence embedding[J].arXiv:1703.03130,2017. |
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