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Author:
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Krallinger, Martin; Rabal, Obdulia; Leitner, Florian; Vázquez, 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; Rocktäschel, Tim; Matos, Sérgio; Campos, David; Tang, Buzhou; Xu, Hua; Munkhdalai, Tsendsuren; Ho Ryu, Keun; Ramanan, SV; Nathan, Senthil; Žitnik, Slavko; Bajec, Marko; Weber, Lutz; Irmer, Matthias; Akhondi, Saber A.; Kors, Jan A.; Xu, Shuo; An, Xin; Kumar Sikdar, Utpal; Ekbal, Asif; Yoshioka, Masaharu; Dieb, Thaer M.; Choi, Miji; Verspoor, Karin; Khabsa, Madian; Lee Giles, C.; Liu, Hongfang; Elayavilli Ravikumar, Komandur; Lamurias, Andre; Coute, Francisco M.; Dai, Hong Jie; Tzong Han Tsai, Richard; Ata, Caglar; Can, Tolga; Usié Chimenos, Anabel; Alves, Rui; Segura Bedmar, Isabel; Martínez, Paloma; Oyarzabal, Julen; Valencia, Alfonso
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Notes:
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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/
This work is supported by the Innovative Medicines Initiative Joint Undertaking (IMI-eTOX) and the MICROME grant 222886-2. |
