FM3S: Features-based measure of sentences semantic similarity

Abstract

The investigation of measuring Semantic Similarity (SS) between sentences is to find a method that can simulate the thinking process of human. In fact, it has become an important task in several applications including Artificial Intelligence and Natural Language Processing. Though this task depends strongly on word SS, the latter is not the only important feature. The current paper presents a new method for computing sentence semantic similarity by exploiting a set of its characteristics, namely Features-based Measure of Sentences Semantic Similarity (FM3S). The proposed method aggregates in a nonlinear function between three components: The noun-based SS including the compound nouns, the verb-based SS using the tense information, and the common word order similarity. It measures the semantic similarity between concepts that play the same syntactic role. Concerning the word-based semantic similarity, an information content-based measure is used to estimate the SS degree between words by exploiting the WordNet "is a" taxonomy. The proposed method yielded into competitive results compared to previously proposed measures with regard to the Li's benchmark, showing a high correlation with human ratings. Further experiments performed on the Microsoft Paraphrase Corpus showed the best Fmeasure values compared to other measures for high similarity thresholds. The results displayed by FM3S prove the importance of syntactic information, compound nouns, and verb tense in the process of computing sentence semantic similarity.