The CHEMDNER corpus of chemicals and drugs and its annotation principles

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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Bibliographic Details
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
Format: Online
Language:English
Published: BioMed Central 2015
Online Access:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4331692/

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https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4331692/

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