The CHEMDNER corpus of chemicals and drugs and its annotation principles

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 cor...

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Detalhes bibliográficos
Publicado no:J Cheminform
Main Authors: 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, Rocktäschel, Tim, Matos, Sérgio, Campos, David, Tang, Buzhou, Xu, Hua, Munkhdalai, Tsendsuren, Ryu, Keun Ho, Ramanan, SV, Nathan, Senthil, Žitnik, 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, Usié, Anabel, Alves, Rui, Segura-Bedmar, Isabel, Martínez, Paloma, Oyarzabal, Julen, Valencia, Alfonso
Formato: Artigo
Idioma:Inglês
Publicado em: BioMed Central 2015
Assuntos:
Research
Acesso em linha:https://ncbi.nlm.nih.gov/pmc/articles/PMC4331692/
https://ncbi.nlm.nih.gov/pubmed/25810773
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1186/1758-2946-7-S1-S2
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