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论文类型：会议论文

发表时间：2014-01-01

收录刊物：CPCI-S、Scopus

页面范围：493-496

关键字：Protein-Protien Interaction; full text; feature selection; syntactic pattern; tree kernel

摘要：Protein-Protein Interaction (PPI) extraction from literatures is becoming a more and more significant task in the biomedical information extraction. Though many methods for PPI extraction have achieved promising results, they all concentrated on the abstracts of literatures rather than full texts. In this paper, we append full-text features, namely Location and Co-occurrence to extract PPIs from full texts. Location describes where the protein pair appears in the article. Co-occurrence is the frequency of each protein pair occurring in the article. In addition, syntactic patterns are extracted as features, and then feature selection is applied to improve the performance and reduce the dimension of feature vectors in SVM. Finally, the selected features are combined with two-level DET tree kernel. Experimental results show that the presented approach can achieve an F-score of 74.46% and an AUC of 78.50%.