Abstract
Traditional algorithms for semantic similarity computation fall into three categories: distance-based, feature contrast and information-based methods. The former two methods ignore the objective statistics, while the last one cannot obtain enough domain data. In this paper, a new method for similarity computation based on Bayesian Estimation is proposed. First, the concept encountering 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. And the posteriori encountering probability is calculated based on Bayesian Estimation. Thereby, the semantic similarity integrating the subjective experience with the objective statistic is acquired based on information-based method. Finally, our method is implemented and the performance is analyzed by WordNet.
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Kui, W., Ling, G., Xianzhong, Z., Jianyu, W. (2009). A Concept Semantic Similarity Algorithm Based on Bayesian Estimation. In: Lee, R., Ishii, N. (eds) Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing. Studies in Computational Intelligence, vol 209. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01203-7_11
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DOI: https://doi.org/10.1007/978-3-642-01203-7_11
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