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 corp...

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Detalles Bibliográficos
Autores principales: Li, Haodi, Tang, Buzhou, Chen, Qingcai, Chen, Kai, Wang, Xiaolong, Wang, Baohua, Wang, Zhe
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2016
Materias:
Original Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4911788/
https://www.ncbi.nlm.nih.gov/pubmed/27270713
http://dx.doi.org/10.1093/database/baw077
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author Li, Haodi
Tang, Buzhou
Chen, Qingcai
Chen, Kai
Wang, Xiaolong
Wang, Baohua
Wang, Zhe
author_facet Li, Haodi
Tang, Buzhou
Chen, Qingcai
Chen, Kai
Wang, Xiaolong
Wang, Baohua
Wang, Zhe
author_sort Li, Haodi
collection PubMed
description 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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spelling pubmed-49117882016-06-20 HITSZ_CDR: an end-to-end chemical and disease relation extraction system for BioCreative V Li, Haodi Tang, Buzhou Chen, Qingcai Chen, Kai Wang, Xiaolong Wang, Baohua Wang, Zhe Database (Oxford) Original Article 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 Oxford University Press 2016-06-05 /pmc/articles/PMC4911788/ /pubmed/27270713 http://dx.doi.org/10.1093/database/baw077 Text en © The Author(s) 2016. Published by Oxford University Press. http://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Original Article
Li, Haodi
Tang, Buzhou
Chen, Qingcai
Chen, Kai
Wang, Xiaolong
Wang, Baohua
Wang, Zhe
HITSZ_CDR: an end-to-end chemical and disease relation extraction system for BioCreative V
title HITSZ_CDR: an end-to-end chemical and disease relation extraction system for BioCreative V
title_full HITSZ_CDR: an end-to-end chemical and disease relation extraction system for BioCreative V
title_fullStr HITSZ_CDR: an end-to-end chemical and disease relation extraction system for BioCreative V
title_full_unstemmed HITSZ_CDR: an end-to-end chemical and disease relation extraction system for BioCreative V
title_short HITSZ_CDR: an end-to-end chemical and disease relation extraction system for BioCreative V
title_sort hitsz_cdr: an end-to-end chemical and disease relation extraction system for biocreative v
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4911788/
https://www.ncbi.nlm.nih.gov/pubmed/27270713
http://dx.doi.org/10.1093/database/baw077
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