HITSZ_CDR: an end-to-end chemical and disease relation extraction system for BioCreative V. (5th June 2016)

• Record Type: Journal Article
• Authors: Li, Haodi; Tang, Buzhou; Chen, Qingcai; Chen, Kai; Wang, Xiaolong; Wang, Baohua; Wang, Zhe
• Is Part Of: Database. Volume 2016(2016)
• Publication Date: 2016-06-05
• Subjects: Biology -- Databases -- Periodicals; Bioinformatics -- Periodicals; 570.285
• DOI: 10.1093/database/baw077
• Languages: English
• ISSNs: 1758-0463

Abstract

Abstract : 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