Weighted Semantic Similarity Assessment Using WordNet

Word and concept similarity assessment is one of the most important elements in natural language processing and information and knowledge retrieval. WordNet, as a popular concept hierarchy, is used in many such applications. Similarity of words in WordNet is also considered in recent researches. Many researches that use WordNet, have calculated similarity between each pair-word by considering Depth of Subsumer of the words and Shortest Path between them. In this paper we have improved semantic similarity measure by giving weights to edges of WordNet hierarchy. We have considered that the nearer an edge is to the root in the hierarchy, the less effect it has in calculating the similarity. Therefore, we have offered a new formula for weighting the edges of hierarchy and based on that calculated the distance between two words and depth of words; and then tuned parameters of the transfer functions using particle swarm optimization. Our experimental results on a common benchmark created by human judgment, show that the resultant correlation has been improved.