Multi-sense Embeddings through a Word Sense Disambiguation Process

Abstract

Natural Language Understanding has seen an increasing number of publications in the last years, especially after robust word embedding models became popular. These models gained a special place in the spotlight when they proved themselves able to capture and represent semantic relations underneath huge amounts of data. Nevertheless, traditional models often fall short in intrinsic issues of linguistics, such as polysemy and homonymy. Multi-sense word embeddings were devised to alleviate these and other problems by representing each word-sense separately, but studies in this area are still in its infancy and much can be explored. We follow this scenario by proposing an unsupervised technique that disambiguates and annotates words by their specific sense, considering their context influence. These are later used to train a word embeddings model to produce a more accurate vector representation. We test our approach in 6 different benchmarks for the word similarity task, showing that our approach can sustain good results and often outperforms current state-of-the-art systems.