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

Database (Oxford)

Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, China.

Published: January 2017

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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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4911788PMC
http://dx.doi.org/10.1093/database/baw077DOI Listing

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