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OGER++: hybrid multi-type entity recognition
2019
Journal of Cheminformatics
We present a text-mining tool for recognizing biomedical entities in scientific literature. OGER++ is a hybrid system for named entity recognition and concept recognition (linking), which combines a dictionary-based annotator with a corpus-based disambiguation component. The annotator uses an efficient look-up strategy combined with a normalization method for matching spelling variants. The disambiguation classifier is implemented as a feed-forward neural network which acts as a postfilter to
doi:10.1186/s13321-018-0326-3
pmid:30666476
pmcid:PMC6689863
fatcat:mwhlaw5aqrhphbda7ze3dx6f5e