Journal article
CHEMDNER: The drugs and chemical names extraction challenge
Martin Krallinger, Florian Leitner, Obdulia Rabal, Miguel Vazquez, Julen Oyarzabal, Alfonso Valencia
JOURNAL OF CHEMINFORMATICS | BMC | Published : 2015
Abstract
Natural language processing (NLP) and text mining technologies for the chemical domain (ChemNLP or chemical text mining) are key to improve the access and integration of information from unstructured data such as patents or the scientific literature. Therefore, the BioCreative organizers posed the CHEMDNER (chemical compound and drug name recognition) community challenge, which promoted the development of novel, competitive and accessible chemical text mining systems. This task allowed a comparative assessment of the performance of various methodologies using a carefully prepared collection of manually labeled text prepared by specially trained chemists as Gold Standard data. We evaluated tw..
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Awarded by MICROME grant
Funding Acknowledgements
We would like to thank David Salgado for his assistance during the chemical annotation process of chemical mentions using the MyMiner system. This work is supported by the Innovative Medicines Initiative Joint Undertaking (IMI-eTOX) and the MICROME grant 222886-2.