The CHEMDNER corpus of chemicals and drugs and its annotation principles

Krallinger, Martin; Rabal, Obdulia; Leitner, Florian; Vázquez, Miguel; Salgado, David; Lu, Zhiyong; Leaman, Robert; Lu, Yanan; Ji, Donghong; Batista-Navarro, Riza Theresa; Sayle, Roger A.; Lowen, Daniel M.; Rak, Rafa; Huber, Torsten; Rocktäschel, Tim; Matos, Srgio; Campos, David; Tahg, Buzhou; Xu, Hua; Munkhdalai, Tsendsuren; Ryu, Keun Ho; Romanan, S.V.; Nathan, Senthil; Žitnik, Slavko; Bajec, Marko; Weber, Lutz; Irmer, Matthias; Saber A., Akhondi; Kors, Jan A.; Xu, Shuo; An, Xin; Sikdar, Utpal Kumar; Ekbal, Asif; Yoshioka, Masaharu; Dieb, Thaer M.; Choi, Miji; Verspoor, Karin; Khabsa, Madian; Giles, C. Lee; Liu, Hongfang; Ravikumar, Komandur Elayavilli; Lamurias, Andre; Couto, Francisco M.; Dai, Hong-Jie; Tsai, Richard Tzong-Han; Ata, Caglar; Can, Tolga; Usié, Anabel; Alves, Rui; Segura-Bedmar, Isabel; Martínez, Paloma; Oyarzábal, Julen y Valencia, Alfonso (2015). The CHEMDNER corpus of chemicals and drugs and its annotation principles. "Journal of Cheminformatics", v. 7 (n. 1); pp.. ISSN 1758-2946. https://doi.org/10.1186/1758-2946-7-S1-S2.

Descripción

Título: The CHEMDNER corpus of chemicals and drugs and its annotation principles
Autor/es:
  • Krallinger, Martin
  • Rabal, Obdulia
  • Leitner, Florian
  • Vázquez, Miguel
  • Salgado, David
  • Lu, Zhiyong
  • Leaman, Robert
  • Lu, Yanan
  • Ji, Donghong
  • Batista-Navarro, Riza Theresa
  • Sayle, Roger A.
  • Lowen, Daniel M.
  • Rak, Rafa
  • Huber, Torsten
  • Rocktäschel, Tim
  • Matos, Srgio
  • Campos, David
  • Tahg, Buzhou
  • Xu, Hua
  • Munkhdalai, Tsendsuren
  • Ryu, Keun Ho
  • Romanan, S.V.
  • Nathan, Senthil
  • Žitnik, Slavko
  • Bajec, Marko
  • Weber, Lutz
  • Irmer, Matthias
  • Saber A., Akhondi
  • Kors, Jan A.
  • Xu, Shuo
  • An, Xin
  • Sikdar, Utpal Kumar
  • Ekbal, Asif
  • Yoshioka, Masaharu
  • Dieb, Thaer M.
  • Choi, Miji
  • Verspoor, Karin
  • Khabsa, Madian
  • Giles, C. Lee
  • Liu, Hongfang
  • Ravikumar, Komandur Elayavilli
  • Lamurias, Andre
  • Couto, Francisco M.
  • Dai, Hong-Jie
  • Tsai, Richard Tzong-Han
  • Ata, Caglar
  • Can, Tolga
  • Usié, Anabel
  • Alves, Rui
  • Segura-Bedmar, Isabel
  • Martínez, Paloma
  • Oyarzábal, Julen
  • Valencia, Alfonso
Tipo de Documento: Artículo
Título de Revista/Publicación: Journal of Cheminformatics
Fecha: 2015
Volumen: 7
Materias:
Palabras Clave Informales: Named entity recognition; BioCreative; Text mining; Chemical entity recognition; Machine learning; Chemical indexing; ChemNLP
Escuela: E.T.S. de Ingenieros Informáticos (UPM)
Departamento: Inteligencia Artificial
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

The automatic extraction of chemical information from text requires the recognition of chemical entity mentions as one of its key steps. When developing supervised named entity recognition (NER) systems, the availability of a large, manually annotated text corpus is desirable. Furthermore, large corpora permit the robust evaluation and comparison of different approaches that detect chemicals in documents. We present the CHEMDNER corpus, a collection of 10,000 PubMed abstracts that contain a total of 84,355 chemical entity mentions labeled manually by expert chemistry literature curators, following annotation guidelines specifically defined for this task. The abstracts of the CHEMDNER corpus were selected to be representative for all major chemical disciplines. Each of the chemical entity mentions was manually labeled according to its structure-associated chemical entity mention (SACEM) class: abbreviation, family, formula, identifier, multiple, systematic and trivial. The difficulty and consistency of tagging chemicals in text was measured using an agreement study between annotators, obtaining a percentage agreement of 91. For a subset of the CHEMDNER corpus (the test set of 3,000 abstracts) we provide not only the Gold Standard manual annotations, but also mentions automatically detected by the 26 teams that participated in the BioCreative IV CHEMDNER chemical mention recognition task. In addition, we release the CHEMDNER silver standard corpus of automatically extracted mentions from 17,000 randomly selected PubMed abstracts. A version of the CHEMDNER corpus in the BioC format has been generated as well. We propose a standard for required minimum information about entity annotations for the construction of domain specific corpora on chemical and drug entities. The CHEMDNER corpus and annotation guidelines are available at: http://www.biocreative.org/resources/biocreative-iv/chemdner-corpus/

Más información

ID de Registro: 41177
Identificador DC: http://oa.upm.es/41177/
Identificador OAI: oai:oa.upm.es:41177
Identificador DOI: 10.1186/1758-2946-7-S1-S2
URL Oficial: https://jcheminf.springeropen.com/articles/10.1186/1758-2946-7-S1-S2
Depositado por: Memoria Investigacion
Depositado el: 07 Nov 2016 13:27
Ultima Modificación: 07 Nov 2016 13:27
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