Computation of the Semantic Relatedness between Words using Concept Clouds

Determining the semantic relatedness between two words refers to computing a statistical measure of similarity between those words. Word similarity measures are useful in a wide range of applications such as natural language processing, query recommendation, relation extraction, spelling correction, document comparison and other information retrieval tasks. Although several methods that address this problem have been proposed in the past, effective computation of semantic relatedness still remains a challenging task. In this paper, we propose a new technique for computing the relatedness between two words. In our approach, instead of computing the relatedness between the two words directly, we propose to first compute the relatedness between their generated concept clouds using web-based coefficients. Next, we use the obtained measure to determine the relatedness between the original words. Our approach heavily relies on a concept extraction algorithm that extracts concepts related to a given query and generates a concept cloud for the query concept. We perform an evaluation on the Miller-Charles benchmark dataset and obtain a correlation coefficient of 0.882, which is better than the correlation coefficients of all other existing state of art methods, hence providing evidence for the effectiveness of our method.