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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Detalles Bibliográficos
Autores principales: 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: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2015
Materias:
Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4331692/
https://www.ncbi.nlm.nih.gov/pubmed/25810773
http://dx.doi.org/10.1186/1758-2946-7-S1-S2

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4331692/
https://www.ncbi.nlm.nih.gov/pubmed/25810773
http://dx.doi.org/10.1186/1758-2946-7-S1-S2

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