The article is published in Expert Systems with Applications (WoS IF: 5.452, SCOPUS).
This paper proposes a variation of a well-known unsupervised graph-based word sense disambiguation method that utilizes all possible semantic information from a used lexical resource to increase graph-semantic connectivity for identifying the intended meanings of words in a given context. If the words have multiple potential meanings (senses) based on context, the proposed method builds an expanded graph representing most relevant semantic information of the words to be disambiguated. Empirical results on benchmark datasets demonstrate that the proposed method outperforms all compared state-of-the-art graph-based word sense disambiguation approaches reported. We also report results obtained by applying the proposed method to a sentiment analysis task.