  Natural Language Understanding has seen an increasing number of publications
in the last few years, especially after robust word embeddings models became
prominent, when they proved themselves able to capture and represent semantic
relationships from massive amounts of data. Nevertheless, traditional models
often fall short in intrinsic issues of linguistics, such as polysemy and
homonymy. Any expert system that makes use of natural language in its core, can
be affected by a weak semantic representation of text, resulting in inaccurate
outcomes based on poor decisions. To mitigate such issues, we propose a novel
approach called Most Suitable Sense Annotation (MSSA), that disambiguates and
annotates each word by its specific sense, considering the semantic effects of
its context. Our approach brings three main contributions to the semantic
representation scenario: (i) an unsupervised technique that disambiguates and
annotates words by their senses, (ii) a multi-sense embeddings model that can
be extended to any traditional word embeddings algorithm, and (iii) a recurrent
methodology that allows our models to be re-used and their representations
refined. We test our approach on six different benchmarks for the word
similarity task, showing that our approach can produce state-of-the-art results
and outperforms several more complex state-of-the-art systems.
