title: Biomedical relation extraction: from binary to complex.
abstract: Biomedical relation extraction aims to uncover high-quality relations from life science literature with high accuracy and efficiency. Early biomedical relation extraction tasks focused on capturing binary relations, such as protein-protein interactions, which are crucial for virtually every process in a living cell. Information about these interactions provides the foundations for new therapeutic approaches. In recent years, more interests have been shifted to the extraction of complex relations such as biomolecular events. While complex relations go beyond binary relations and involve more than two arguments, they might also take another relation as an argument. In the paper, we conduct a thorough survey on the research in biomedical relation extraction. We first present a general framework for biomedical relation extraction and then discuss the approaches proposed for binary and complex relation extraction with focus on the latter since it is a much more difficult task compared to binary relation extraction. Finally, we discuss challenges that we are facing with complex relation extraction and outline possible solutions and future directions.
authors.affiliation: School of Computer Science and Engineering, Key Laboratory of Computer Network and Information Integration, Ministry of Education, Southeast University, Nanjing 210096, China.
authors.forename: Deyu
authors.identifier: 0000-0001-7702-9387
authors.initials: D
authors.lastname: Zhou
authors.affiliation: School of Computer Science and Engineering, Key Laboratory of Computer Network and Information Integration, Ministry of Education, Southeast University, Nanjing 210096, China.
authors.forename: Dayou
authors.initials: D
authors.lastname: Zhong
authors.affiliation: School of Engineering and Applied Science, Aston University, Birmingham B4 7ET, UK.
authors.forename: Yulan
authors.identifier: 0000-0003-3948-5845
authors.initials: Y
authors.lastname: He
mesh_terms: D001695:Biology / Q000379:methods*; D057225:Data Mining / Q000379:methods*; D006801:Humans; D008511:Medicine / Q000379:methods*