Dataset Information

The CHEMDNER corpus of chemicals and drugs and its annotation principles.

ABSTRACT: 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/.

SUBMITTER: Krallinger M

PROVIDER: S-EPMC4331692 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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The CHEMDNER corpus of chemicals and drugs and its annotation principles.

Krallinger Martin M Rabal Obdulia O Leitner Florian F Vazquez Miguel M Salgado David D Lu Zhiyong Z Leaman Robert R Lu Yanan Y Ji Donghong D Lowe Daniel M DM Sayle Roger A RA Batista-Navarro Riza Theresa RT Rak Rafal R Huber Torsten T Rocktäschel Tim T Matos Sérgio S Campos David D Tang Buzhou B Xu Hua H Munkhdalai Tsendsuren T Ryu Keun Ho KH Ramanan S V SV Nathan Senthil S Žitnik Slavko S Bajec Marko M Weber Lutz L Irmer Matthias M Akhondi Saber A SA Kors Jan A JA Xu Shuo S An Xin X Sikdar Utpal Kumar UK Ekbal Asif A Yoshioka Masaharu M Dieb Thaer M TM Choi Miji M Verspoor Karin K Khabsa Madian M Giles C Lee CL Liu Hongfang H Ravikumar Komandur Elayavilli KE Lamurias Andre A Couto Francisco M FM Dai Hong-Jie HJ Tsai Richard Tzong-Han RT Ata Caglar C Can Tolga T Usié Anabel A Alves Rui R Segura-Bedmar Isabel I Martínez Paloma P Oyarzabal Julen J Valencia Alfonso A

Journal of cheminformatics 20150119 Suppl 1 Text mining for chemistry and the CHEMDNER trac

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 tota ...[more]

PMID: 25810773

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Project description:Manually annotated corpora are critical for the training and evaluation of automated methods to identify concepts in biomedical text.This paper presents the concept annotations of the Colorado Richly Annotated Full-Text (CRAFT) Corpus, a collection of 97 full-length, open-access biomedical journal articles that have been annotated both semantically and syntactically to serve as a research resource for the biomedical natural-language-processing (NLP) community. CRAFT identifies all mentions of nearly all concepts from nine prominent biomedical ontologies and terminologies: the Cell Type Ontology, the Chemical Entities of Biological Interest ontology, the NCBI Taxonomy, the Protein Ontology, the Sequence Ontology, the entries of the Entrez Gene database, and the three subontologies of the Gene Ontology. The first public release includes the annotations for 67 of the 97 articles, reserving two sets of 15 articles for future text-mining competitions (after which these too will be released). Concept annotations were created based on a single set of guidelines, which has enabled us to achieve consistently high interannotator agreement.As the initial 67-article release contains more than 560,000 tokens (and the full set more than 790,000 tokens), our corpus is among the largest gold-standard annotated biomedical corpora. Unlike most others, the journal articles that comprise the corpus are drawn from diverse biomedical disciplines and are marked up in their entirety. Additionally, with a concept-annotation count of nearly 100,000 in the 67-article subset (and more than 140,000 in the full collection), the scale of conceptual markup is also among the largest of comparable corpora. The concept annotations of the CRAFT Corpus have the potential to significantly advance biomedical text mining by providing a high-quality gold standard for NLP systems. The corpus, annotation guidelines, and other associated resources are freely available at http://bionlp-corpora.sourceforge.net/CRAFT/index.shtml.

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Dataset Information

The CHEMDNER corpus of chemicals and drugs and its annotation principles.

Publications

The CHEMDNER corpus of chemicals and drugs and its annotation principles.

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