The CHEMDNER corpus of chemicals and drugs and its annotation principles

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10.1186:1758-2946-7-S1-S2.pdf

Date

2015-01-19

Author

Krallinger, Martin
Rabal, Obdulia
Leitner, Florian
Vazquez, Miguel
Salgado, David
Lu, Zhiyong
Leaman, Robert
Lu, Yanan
Ji, Donghong
Lowe, Daniel M.
Sayle, Roger A.
Batista-Navarro, Riza Theresa
Rak, Rafal
Huber, Torsten
Rocktaschel, Tim
Matos, Serergio
Campos, David
Tang, Buzhou
Xu, Hua
Munkhdalai, Tsendsuren
Ryu, Keun Ho
Ramanan, S. V.
Nathan, Senthil
Zitnik, Slavko
Bajec, Marko
Weber, Lutz
Irmer, Matthias
Akhondi, Saber A.
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
Usie, Anabel
Alves, Rui
Segura-Bedmar, Isabel
Martinez, Paloma
Oyarzabal, Julen
Valencia, Alfonso

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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/

Subject Keywords

NAMED ENTITY RECOGNITION, TEXT, EXTRACTION

URI

https://hdl.handle.net/11511/46911

Journal

JOURNAL OF CHEMINFORMATICS

DOI

https://doi.org/10.1186/1758-2946-7-s1-s2

Collections

Department of Computer Engineering, Article

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Citation Formats

M. Krallinger et al., “The CHEMDNER corpus of chemicals and drugs and its annotation principles,” JOURNAL OF CHEMINFORMATICS, pp. 0–0, 2015, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/46911.