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.

提出了一种名为Most Suitable Sense Annotation (MSSA)的新方法，它通过一种无监督技术来标注每个单词的特定含义，并考虑其上下文的语义效应，从而减轻了自然语言理解中多义性和同音异义词的问题，实现了语义表示方面的三个主要贡献，使用六个不同的基准模型进行word similarity测试，结果表明该方法能够产生最先进的结果，胜过了几个更复杂的先进系统。

通过词义消歧过程实现多义词嵌入