A Concept Semantic Similarity Algorithm Based on Bayesian Estimation
Jiabao Zhao · Zhongwen xinxi xuebao · 2010
Traditional algorithms for semantic similarity computation fall into two categories: distance-based and information-based methods.The former ignores the objective statistics,while the latter suffers from insufficient domain data.In this paper,a new method for similarity computation based on Bayesian Estimation is proposed.First,the concept emergence probability is assumed to be a random variable with a priori Beta distribution.Second,its priori parameters are designated by the distance-based similarity algorithm,calculated by Bayesian Estimation.Thereby,the semantic similarity integrating the subjective experience with the objective statistic is acquired based on information-based method.Finally,the proposed method is implemented and proved by a slightly higher correlation with human judgments against WordNet.