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Putting hands to rest: efficient deep CNN-RNN architecture for chemical named entity recognition with no hand-crafted rules
2018
Journal of Cheminformatics
Chemical named entity recognition (NER) is an active field of research in biomedical natural language processing. To facilitate the development of new and superior chemical NER systems, BioCreative released the CHEMDNER corpus, an extensive dataset of diverse manually annotated chemical entities. Most of the systems trained on the corpus rely on complicated hand-crafted rules or curated databases for data preprocessing, feature extraction and output post-processing, though modern machine
doi:10.1186/s13321-018-0280-0
pmid:29796778
pmcid:PMC5966369
fatcat:47rjjknesragtjuwapu3jqiz6m