title: Exploiting syntactic and semantics information for chemical-disease relation extraction.
abstract: Identifying chemical-disease relations (CDR) from biomedical literature could improve chemical safety and toxicity studies. This article proposes a novel syntactic and semantic information exploitation method for CDR extraction. The proposed method consists of a feature-based model, a tree kernel-based model and a neural network model. The feature-based model exploits lexical features, the tree kernel-based model captures syntactic structure features, and the neural network model generates semantic representations. The motivation of our method is to fully utilize the nice properties of the three models to explore diverse information for CDR extraction. Experiments on the BioCreative V CDR dataset show that the three models are all effective for CDR extraction, and their combination could further improve extraction performance.Database URL:http://www.biocreative.org/resources/corpora/biocreative-v-cdr-corpus/.
authors.affiliation: School of Computer Science and Technology, Dalian University of Technology, Dalian 116024, People's Republic of China zhouhuiwei@dlut.edu.cn.
authors.forename: Huiwei
authors.initials: H
authors.lastname: Zhou
authors.affiliation: School of Computer Science and Technology, Dalian University of Technology, Dalian 116024, People's Republic of China.
authors.forename: Huijie
authors.initials: H
authors.lastname: Deng
authors.affiliation: School of Computer Science and Technology, Dalian University of Technology, Dalian 116024, People's Republic of China.
authors.forename: Long
authors.initials: L
authors.lastname: Chen
authors.affiliation: School of Computer Science and Technology, Dalian University of Technology, Dalian 116024, People's Republic of China.
authors.forename: Yunlong
authors.initials: Y
authors.lastname: Yang
authors.affiliation: School of Computer Science and Technology, Dalian University of Technology, Dalian 116024, People's Republic of China.
authors.forename: Chen
authors.initials: C
authors.lastname: Jia
authors.affiliation: School of Computer Science and Technology, Dalian University of Technology, Dalian 116024, People's Republic of China.
authors.forename: Degen
authors.initials: D
authors.lastname: Huang
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D057225:Data Mining / Q000379:methods*; D016208:Databases, Factual; D004194:Disease / Q000209:etiology*; D015386:Hazardous Substances / Q000633:toxicity*; D006801:Humans; D020407:Internet; D016571:Neural Networks, Computer; D012660:Semantics
chemical_list: D015386:Hazardous Substances