title: Disease named entity recognition by combining conditional random fields and bidirectional recurrent neural networks.
abstract: 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.Database URL: http://219.223.252.210:8080/SS/cdr.html.
authors: Shenzhen Engineering Laboratory of Performance Robots at Digital Stage, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, China and.
authors: Qikang
authors: Q
authors: Wei
authors: Shenzhen Engineering Laboratory of Performance Robots at Digital Stage, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, China and.
authors: Tao
authors: T
authors: Chen
authors: Shenzhen Engineering Laboratory of Performance Robots at Digital Stage, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, China and xuruifeng@hitsz.edu.cn.
authors: Ruifeng
authors: R
authors: Xu
authors: School of Engineering and Applied Science, Aston University, Birmingham, UK.
authors: Yulan
authors: Y
authors: He
authors: Shenzhen Engineering Laboratory of Performance Robots at Digital Stage, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, China and.
authors: Lin
authors: L
authors: Gui
mesh_terms: D000818:Animals; D057225:Data Mining / Q000379:methods*; D004194:Disease; D006801:Humans; D008962:Models, Theoretical; D009323:Natural Language Processing; D016571:Neural Networks, Computer; D060388:Support Vector Machine; D009626:Terminology as Topic