基于CP加权的概念语义相似度算法

Concept Semantic Similarity Algorithm Based On CP Weighted

对Worde Net中的概念语义相似度算法进行了研究,分析了Worde Net中影响语义相似度算法的相关因素,利用Worde Net中is-a关系构成的概念语义层次结构,提出了一种新的CP(Conditional Probabilities)加权方法,将一个概念出现的情况下另一个概念出现的概率(条件概率)作为衡量权重大小的参数,并把上、下位关系间的权重区分开来,使权重分配更加合理,并在此基础上给出了概念语义相似度算法的改进模型。该模型以概念的信息内容参数为基础,不仅考虑了概念所处分类树的密度和深度,同时也将概念对间的路径考虑在内,使概念间语义相似度的准确性有了一定的提高。在与其它算法进行比较后,实验结果显示该算法模型不仅具有更高的精确度,而且改善了传统方法离散程度高的缺点。

Research on semantic similarity algorithm between concepts in Worde Net is done in this paper, the related factors of Worde Net are analyzed which affected concept semantic similarity algorithm. a new CP weighted method is proposed by using the semantic hierarchy among concepts which consists of is-a relations in Worde Net, The method takes the probability of one concept appearing when another concept appearing（conditional probability） as a parameter used to measure the weight of hyponymy between concepts, the weights of hypernym and hypogyny are distinguished, so that the weight distribution is more reasonable. The improved model of concept semantic similarity algorithm is given based on the method. The model is not only considers the concept of the density and depth of the class tree, but also takes the inter path between the two concepts into consideration, so that the accuracy of semantic similarity between concepts has been improved. Comparing with other algorithms, the experimental results show that the proposed algorithm not only has a higher accuracy, but also improves the high discrete degree of traditional method and it is more simple and intuitive.