Evaluating Word Representation Features in Biomedical Named Entity Recognition Tasks
Joint Authors
Cao, Hongxin
Wang, Xiaolong
Xu, Hua
Chen, Qingcai
Tang, Buzhou
Source
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-6, 6 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-03-06
Country of Publication
Egypt
No. of Pages
6
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Tang, Buzhou& Cao, Hongxin& Wang, Xiaolong& Chen, Qingcai& Xu, Hua. 2014. Evaluating Word Representation Features in Biomedical Named Entity Recognition Tasks. BioMed Research International،Vol. 2014, no. 2014, pp.1-6.
https://search.emarefa.net/detail/BIM-456543
Modern Language Association (MLA)
Tang, Buzhou…[et al.]. Evaluating Word Representation Features in Biomedical Named Entity Recognition Tasks. BioMed Research International No. 2014 (2014), pp.1-6.
https://search.emarefa.net/detail/BIM-456543
American Medical Association (AMA)
Tang, Buzhou& Cao, Hongxin& Wang, Xiaolong& Chen, Qingcai& Xu, Hua. Evaluating Word Representation Features in Biomedical Named Entity Recognition Tasks. BioMed Research International. 2014. Vol. 2014, no. 2014, pp.1-6.
https://search.emarefa.net/detail/BIM-456543
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-456543