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论文类型：期刊论文

发表时间：2016-07-01

发表刊物：IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS

收录刊物：SCIE、EI、Scopus

卷号：13

期号：4

页面范围：669-677

ISSN号：1545-5963

关键字：Biomedical event extraction; dual decomposition; word embeddings; natural language processing

摘要：Extracting biomedical event from literatures has attracted much attention recently. By now, most of the state-of-the-art systems have been based on pipelines which suffer from cascading errors, and the words encoded by one-hot are unable to represent the semantic information. Joint inference with dual decomposition and novel word embeddings are adopted to address the two problems, respectively, in this work. Word embeddings are learnt from large scale unlabeled texts and integrated as an unsupervised feature into other rich features based on dependency parse graphs to detect triggers and arguments. The proposed system consists of four components: trigger detector, argument detector, jointly inference with dual decomposition, and rule-based semantic post-processing, and outperforms the state-of-the-art systems. On the development set of BioNLP'09, the F-score is 59.77 percent on the primary task, which is 0.96 percent higher than the best system. On the test set of BioNLP'11, the F-score is 56.09 and 0.89 percent higher than the best published result that do not adopt additional techniques. On the test set of BioNLP'13, the F-score reaches 53.19 percent which is 2.22 percent higher than the best result.