The CHEMDNER corpus of chemicals and drugs and its annotation principles

Description: 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...
Language(s): Inglés
Subject(s): Named entity recognition , BioCreative , Text mining , Chemical entity recognition , Machine learning , Chemical indexing , ChemNLP
Publisher(s): Chemistry Central
Contributor(s):
Source(s):
Publication Date(s): 2015-01-01
Type(s): Artículo científico antes de ser publicado, versión del editor (article)
Rights(s): info:eu-repo/semantics/openAccess
Relation(s): http://dx.doi.org/10.1186/1758-2946-7-S1-S2 , info:eu-repo/grantAgreement/EC/FP7/115002;222886
Estadísticas: Ocultar/Mostrar estadísticas
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