Affiliations: [a] Department of Computer Science and Technology, Shenyang University of Chemical Technology, Shenyang, Liaoning, China | [b] Department of Informatics University of Leicester, Leicester, England, LE1 7RH, UK
Correspondence:
[*]
Corresponding author: Hui Guan, Department of Computer Science and Technology, Shenyang University of Chemical Technology, Shenyang, Liaoning, China. E-mail: [email protected].
Abstract: Since computing semantic similarity tends to simulate the thinking process of humans, semantic dissimilarity must play a part in this process. In this paper, we present a new approach for semantic similarity measuring by taking consideration of dissimilarity into the process of computation. Specifically, the proposed measures explore the potential antonymy in the hierarchical structure of WordNet to represent the dissimilarity between concepts and then combine the dissimilarity with the results of existing methods to achieve semantic similarity results. The relation between parameters and the correlation value is discussed in detail. The proposed model is then applied to different text granularity levels to validate the correctness on similarity measurement. Experimental results show that the proposed approach not only achieves high correlation value against human ratings but also has effective improvement to existing path-distance based methods on the word similarity level, in the meanwhile effectively correct existing sentence similarity method in some cases in Microsoft Research Paraphrase Corpus and SemEval-2014 date set.
