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