基于贝叶斯估计的概念语义相似度算法

传统的基于语义距离的概念语义相似度算法不能兼顾客观统计数据,基于信息量的相似度算法又难以获得权威统计样本,针对这些不足,该文提出一种基于贝叶斯估计的概念语义相似度算法。该算法首先假定概念出现概率是符合Beta分布的随机变量,然后基于语义距离的相似度算法计算先验参数,并根据统计样本计算该先验分布下基于最小风险的贝叶斯估计后验参数。随后利用基于信息量的语义相似度算法,便可获得主观经验与客观事实相结合的概念语义相似度。结合WordNet的实验分析表明,该算法与人为主观经验之间具有最大的相关系数。

A Concept Semantic Similarity Algorithm Based on Bayesian Estimation

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 algo...