G2WS: Gloss-based WordNet and Wiktionary semantic Similarity measure

Computing Semantic Similarity (SS) between words is an important issue of many research fields. Several features from knowledge databases can be involved in the definition of semantic computing models. Gloss-based approaches exploit the short and precise description for a concept for better expressing its semantics. In this paper, we propose a new Gloss-based WordNet and Wiktionary semantic Similarity (G2WS) measure between words. In fact, the computational model is based on a noun factors computed using the glosses of the ancestors assigned to a specific concept within the WordNet “is a” taxonomy. The factor_W of each noun is provided using an Information Content (IC) measure. The set of glosses assigned to each concept in WordNet are enriched by those from Wiktionary. Our work has been evaluated and compared with related works using a wide set of benchmarks conceived for word semantic similarity task. Obtained results show that the new similarity measure correlated better with human judgments than related works.