  Word embeddings are numerical vectors which can represent words or concepts
in a low-dimensional continuous space. These vectors are able to capture useful
syntactic and semantic information. The traditional approaches like Word2Vec,
GloVe and FastText have a strict drawback: they produce a single vector
representation per word ignoring the fact that ambiguous words can assume
different meanings. In this paper we use techniques to generate sense
embeddings and present the first experiments carried out for Portuguese. Our
experiments show that sense vectors outperform traditional word vectors in
syntactic and semantic analogy tasks, proving that the language resource
generated here can improve the performance of NLP tasks in Portuguese.
