Statistical and Semantic Similarity between English Sentences

This book presents various algorithms to compute semantic similarities between english texts. I explored three different algorithms for computing English sentence similarity. The first algorithm which is well-explored in the literature [Salton and Buckley 1988 Wu and Salton 1981] weights words in each sentence according to term frequency and inverse document frequency (tf-idf ) and uses no semantic information. The second algorithm uses measures of the semantic distance between words belonging to the same part of speech. The third algorithm combines the tf-idf scores and the semantic distance scores between words. I evaluated the performance of the second and third algorithms on two data sets: O'Shea's set of sentence pairs with human similarity judgements [Li et al. Aug Rubenstein and Goodenough 1965] and Microsoft Research's sentence-level paraphrase dataset [Rus et al. 2012]. On O'Shea's data set the third algorithm more accurately matches human judgments than the second. On the Microsoft data set there was not a significant difference between the two algorithms