Publication: OGER++: hybrid multi-type entity recognition
OGER++: hybrid multi-type entity recognition
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Furrer, L., Jancso, A., Colic, N., & Rinaldi, F. (2019). OGER++: hybrid multi-type entity recognition. Journal of Cheminformatics, 11(1), 7. https://doi.org/10.1186/s13321-018-0326-3
Abstract
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Abstract
Background: 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 the previous step. Results: We
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- Furrer, Lenz
- Jancso, Anna
- Colic, Nicola
- Rinaldi, Fabio
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Furrer, L., Jancso, A., Colic, N., & Rinaldi, F. (2019). OGER++: hybrid multi-type entity recognition. Journal of Cheminformatics, 11(1), 7. https://doi.org/10.1186/s13321-018-0326-3
