Disease named entity recognition by combining conditional random fields and bidirectional recurrent neural networks


The recognition of disease and chemical named entities in scientific articles is a very important subtask in information extraction in the biomedical domain. Due to the diversity and complexity of disease names, the recognition of named entities of diseases is rather tougher than those of chemical names. Although there are some remarkable chemical named entity recognition systems available online such as ChemSpot and tmChem, the publicly available recognition systems of disease named entities are rare. This article presents a system for disease named entity recognition (DNER) and normalization. First, two separate DNER models are developed. One is based on conditional random fields model with a rule-based post-processing module. The other one is based on the bidirectional recurrent neural networks. Then the named entities recognized by each of the DNER model are fed into a support vector machine classifier for combining results. Finally, each recognized disease named entity is normalized to a medical subject heading disease name by using a vector space model based method. Experimental results show that using 1000 PubMed abstracts for training, our proposed system achieves an F1-measure of 0.8428 at the mention level and 0.7804 at the concept level, respectively, on the testing data of the chemical-disease relation task in BioCreative V.

Publication DOI: https://doi.org/10.1093/database/baw140
Divisions: College of Engineering & Physical Sciences > Systems analytics research institute (SARI)
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Additional Information: © The Author(s) 2016. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
Uncontrolled Keywords: General Medicine,Information Systems,General Biochemistry,Genetics and Molecular Biology,General Agricultural and Biological Sciences
Publication ISSN: 1758-0463
Last Modified: 02 Jul 2024 07:05
Date Deposited: 19 Apr 2017 11:25
Full Text Link:
Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2016-10-24
Accepted Date: 2016-09-27
Submitted Date: 2015-12-04
Authors: Wei, Qikang
Chen, Tao
Xu, Ruifeng
He, Yulan (ORCID Profile 0000-0003-3948-5845)
Gui, Lin



Version: Published Version

License: Creative Commons Attribution

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