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Word sense disambiguation helps identifying the proper sense of ambiguouswords in text. With large terminologies such as the UMLS Metathesaurusambiguities appear and highly effective disambiguation methods are required.Supervised learning algorithm methods are used as one of the approaches toperform disambiguation. Features extracted from the context of an ambiguousword are used to identify the proper sense of such a word. The type of featureshave an impact on machine learning methods, thus affect disambiguationperformance. In this work, we have evaluated several types of features derivedfrom the context of the ambiguous word and we have explored as well more globalfeatures derived from MEDLINE using word embeddings. Results show that wordembeddings improve the performance of more traditional features and allow aswell using recurrent neural network classifiers based on Long-Short Term Memory(LSTM) nodes. The combination of unigrams and word embeddings with an SVM setsa new state of the art performance with a macro accuracy of 95.97 in the MSHWSD data set.

Word embeddings and recurrent neural networks based on Long-Short Term Memory nodes in supervised biomedical word sense disambiguation