Large scale biomedical texts classification: a kNN and an ESA-based approaches

no code implementations • 9 Jun 2016 • Khadim Dramé, Fleur Mougin, Gayo Diallo

Furthermore, we investigate if the results of this method could be useful as a complementary feature of our kNN-based approach. ResultsExperimental evaluations performed on large standard annotated datasets, provided by the BioASQ organizers, show that the kNN-based method with the Random Forest learning algorithm achieves good performances compared with the current state-of-the-art methods, reaching a competitive f-measure of 0. 55% while the ESA-based approach surprisingly yielded reserved results. ConclusionsWe have proposed simple classification methods suitable to annotate textual documents using only partial information.

General Classification Information Retrieval +4