  In this paper, we present Watasense, an unsupervised system for word sense
disambiguation. Given a sentence, the system chooses the most relevant sense of
each input word with respect to the semantic similarity between the given
sentence and the synset constituting the sense of the target word. Watasense
has two modes of operation. The sparse mode uses the traditional vector space
model to estimate the most similar word sense corresponding to its context. The
dense mode, instead, uses synset embeddings to cope with the sparsity problem.
We describe the architecture of the present system and also conduct its
evaluation on three different lexical semantic resources for Russian. We found
that the dense mode substantially outperforms the sparse one on all datasets
according to the adjusted Rand index.
