title: An unsupervised text mining method for relation extraction from biomedical literature.
abstract: The wealth of interaction information provided in biomedical articles motivated the implementation of text mining approaches to automatically extract biomedical relations. This paper presents an unsupervised method based on pattern clustering and sentence parsing to deal with biomedical relation extraction. Pattern clustering algorithm is based on Polynomial Kernel method, which identifies interaction words from unlabeled data; these interaction words are then used in relation extraction between entity pairs. Dependency parsing and phrase structure parsing are combined for relation extraction. Based on the semi-supervised KNN algorithm, we extend the proposed unsupervised approach to a semi-supervised approach by combining pattern clustering, dependency parsing and phrase structure parsing rules. We evaluated the approaches on two different tasks: (1) Protein-protein interactions extraction, and (2) Gene-suicide association extraction. The evaluation of task (1) on the benchmark dataset (AImed corpus) showed that our proposed unsupervised approach outperformed three supervised methods. The three supervised methods are rule based, SVM based, and Kernel based separately. The proposed semi-supervised approach is superior to the existing semi-supervised methods. The evaluation on gene-suicide association extraction on a smaller dataset from Genetic Association Database and a larger dataset from publicly available PubMed showed that the proposed unsupervised and semi-supervised methods achieved much higher F-scores than co-occurrence based method.
authors.affiliation: AnHui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine, School of Computer and Information, Hefei University of Technology, Hefei, China.
authors.forename: Changqin
authors.initials: C
authors.lastname: Quan
authors.affiliation: AnHui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine, School of Computer and Information, Hefei University of Technology, Hefei, China.
authors.forename: Meng
authors.initials: M
authors.lastname: Wang
authors.affiliation: AnHui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine, School of Computer and Information, Hefei University of Technology, Hefei, China; Faculty of Engineering, University of Tokushima, 2-1 MinamiJosanjima, Tokushima, Japan.
authors.forename: Fuji
authors.initials: F
authors.lastname: Ren
mesh_terms: D000465:Algorithms; D035843:Biomedical Research; D016000:Cluster Analysis; D057225:Data Mining / Q000379:methods*; D056726:Genetic Association Studies; D006801:Humans; D009323:Natural Language Processing; D010363:Pattern Recognition, Automated; D025941:Protein Interaction Mapping; D039781:PubMed; D011642:Publications; D013405:Suicide