HITSZ_CDR: an end-to-end chemical and disease relation extraction system for BioCreative V.
In this article, an end-to-end system was proposed for the challenge task of disease named entity recognition (DNER) and chemical-induced disease (CID) relation extraction in BioCreative V, where DNER includes disease mention recognition (DMR) and normalization (DN). Evaluation on the challenge corpus showed that our system achieved the highest F1-scores 86.93% on DMR, 84.11% on DN, 43.04% on CID relation extraction, respectively. The F1-score on DMR is higher than our previous one reported by the challenge organizers (86.76%), the highest F1-score of the challenge.Database URL: http://database.oxfordjournals.org/content/2016/baw077.
Intelligent Computing Research Center, Harbin Institute of Technology Shenzhen School, China.
Haodi
H
Li
Intelligent Computing Research Center, Harbin Institute of Technology Shenzhen School, China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, China tangbuzhou@gmail.com.
Buzhou
B
Tang
Intelligent Computing Research Center, Harbin Institute of Technology Shenzhen School, China.
Qingcai
Q
Chen
Intelligent Computing Research Center, Harbin Institute of Technology Shenzhen School, China.
Kai
K
Chen
Intelligent Computing Research Center, Harbin Institute of Technology Shenzhen School, China.
Xiaolong
X
Wang
College of Mathematics and statistics, Shenzhen University, China.
Baohua
B
Wang
Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, China.
Zhe
Z
Wang
D019295:Computational Biology / Q000379:methods*; D057225:Data Mining / Q000379:methods*; D016208:Databases, Factual; D004194:Disease; D055808:Drug Discovery; D064420:Drug-Related Side Effects and Adverse Reactions; D006801:Humans; D000069550:Machine Learning; D014116:Toxicology