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论文类型：会议论文

发表时间：2017-01-01

收录刊物：SCIE、EI、CPCI-S

卷号：10565

页面范围：97-109

关键字：CDR extraction; CNN; Word sequences; Dependency structures

摘要：Understanding chemical-disease relations (CDR) from biomedical literature is important for biomedical research and chemical discovery. This paper uses a k-max pooling convolutional neural network (CNN) to exploit word sequences and dependency structures for CDR extraction. Furthermore, an effective weighted context method is proposed to capture semantic information of word sequences. Our system extracts both intra-and inter-sentence level chemical-disease relations, which are merged as the final CDR. Experiments on the BioCreative V CDR dataset show that both word sequences and dependency structures are effective for CDR extraction, and their integration could further improve the extraction performance.