HITSZ_CDR: an end-to-end chemical and disease relation extraction system for BioCreative V | Zendy

Haodi Li | Zendy; Buzhou Tang | Zendy; Qingcai Chen | Zendy; Kai Chen | Zendy; Xiaolong Wang | Zendy; Baohua Wang | Zendy; Zhe Wang | Zendy

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HITSZ_CDR: an end-to-end chemical and disease relation extraction system for BioCreative V

Author(s) -

Haodi Li,

Buzhou Tang,

Qingcai Chen,

Kai Chen,

Xiaolong Wang,

Baohua Wang,

Zhe Wang

Publication year - 2016

Publication title -

database

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 1.406

H-Index - 62

ISSN - 1758-0463

DOI - 10.1093/database/baw077

Subject(s) - named entity recognition , relationship extraction , normalization (sociology) , computer science , end to end principle , natural language processing , artificial intelligence , information extraction , task (project management) , information retrieval , speech recognition , engineering , systems engineering , sociology , anthropology

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.

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