Statistical and Semantic Similarity between English Sentences
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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 dist...
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
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