title: Evaluating word representation features in biomedical named entity recognition tasks.
abstract: Biomedical Named Entity Recognition (BNER), which extracts important entities such as genes and proteins, is a crucial step of natural language processing in the biomedical domain. Various machine learning-based approaches have been applied to BNER tasks and showed good performance. In this paper, we systematically investigated three different types of word representation (WR) features for BNER, including clustering-based representation, distributional representation, and word embeddings. We selected one algorithm from each of the three types of WR features and applied them to the JNLPBA and BioCreAtIvE II BNER tasks. Our results showed that all the three WR algorithms were beneficial to machine learning-based BNER systems. Moreover, combining these different types of WR features further improved BNER performance, indicating that they are complementary to each other. By combining all the three types of WR features, the improvements in F-measure on the BioCreAtIvE II GM and JNLPBA corpora were 3.75% and 1.39%, respectively, when compared with the systems using baseline features. To the best of our knowledge, this is the first study to systematically evaluate the effect of three different types of WR features for BNER tasks.
authors.affiliation: Department of Computer Science, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong 518055, China ; School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA.
authors.forename: Buzhou
authors.identifier: 0000-0003-0271-8246
authors.initials: B
authors.lastname: Tang
authors.affiliation: Department of Medical Informatics, Second Military Medical University, Shanghai 200433, China.
authors.forename: Hongxin
authors.initials: H
authors.lastname: Cao
authors.affiliation: Department of Computer Science, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong 518055, China.
authors.forename: Xiaolong
authors.initials: X
authors.lastname: Wang
authors.affiliation: Department of Computer Science, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong 518055, China.
authors.forename: Qingcai
authors.initials: Q
authors.lastname: Chen
authors.affiliation: School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA.
authors.forename: Hua
authors.initials: H
authors.lastname: Xu
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D035843:Biomedical Research; D057225:Data Mining / Q000379:methods*; D016239:MEDLINE; D009323:Natural Language Processing; D010363:Pattern Recognition, Automated / Q000379:methods*; D012660:Semantics; D009626:Terminology as Topic; D018875:Vocabulary, Controlled
references.citation: Genome Inform Ser Workshop Genome Inform. 1998;9:72-80
references.pmid: 11072323
references.citation: Pac Symp Biocomput. 1998;:707-18
references.pmid: 9697224
references.citation: Genome Biol. 2008;9 Suppl 2:S2
references.pmid: 18834493
references.citation: Nucleic Acids Res. 2004 Jan 1;32(Database issue):D267-70
references.pmid: 14681409
references.citation: J Am Med Inform Assoc. 2013 Sep-Oct;20(5):828-35
references.pmid: 23571849
references.citation: BMC Med Inform Decis Mak. 2013;13 Suppl 1:S1
references.pmid: 23566040
references.citation: Bioinformatics. 2006 Jan 1;22(1):103-5
references.pmid: 16267085
references.citation: Neural Comput. 2004 Dec;16(12):2639-64
references.pmid: 15516276
references.citation: Brief Bioinform. 2005 Dec;6(4):357-69
references.pmid: 16420734