Sentence Similarity Using Modified Latent Semantic Analysis and Semantic Relations

Abstract

In this paper, we propose a method for finding the similarity of sentence pairs. The method combines two modules – a modified Latent Semantic Analysis and a semantic similarity computation. The proposed method makes use of the syntactic structure and semantic information contained in the sentence pairs. The syntactic structure in the form of dependency triplets is extracted and a semantic similarity calculation is carried out. The semantic similarity between words is calculated using Wu & Palmer similarity measure and Wordnet synonym relation is used in modified Latent Semantic Analysis. The proposed method is evaluated on the Microsoft Research Paraphrase Corpus dataset and the accuracy obtained on the dataset is 73.19% which is better than existing statistical and zero shot domain adaptation methods. The proposed method is also tested on Li et al. text similarity dataset and the Pearson correlation coefficient of 0.9021, Spearman correlation of 0.9103 and mean deviation of 0.105 with the human judgement show that the method outperforms state-of-the-art methods.