Using sentence semantic similarity to improve LMF standardized arabic dictionary quality

Abstract

This paper presents a novel algorithm to measure semantic similarity between sentences. It will introduce a method that takes into account of not only semantic knowledge but also syntactico-semantic knowledge notably semantic predicate, semantic class and thematic role. Firstly, semantic similarity between sentences is derived from words synonymy. Secondly, syntactico-semantic similarity is computed from the common semantic class and thematic role of words in the sentence. Indeed, this information is related to semantic predicate. Finally, semantic similarity is computed as a combination of lexical similarity, semantic similarity and syntactico-semantic similarity using a supervised learning. The proposed algorithm is applied to detect the information redundancy in LMF Arabic dictionary especially the definitions and the examples of lexical entries. Experimental results show that the proposed algorithm reduces the redundant information to improve the content quality of LMF Arabic dictionary.