A multifaceted approach to sentence similarity

Abstract

We propose a novel method for measuring semantic similarity between two sentences. The method exploits both syntactic and semantic features to assess the similarity. In our method, words in a sentence are weighted using their information content. The weights of words help differentiate their contribution towards the meaning of the sentence. The originality of this research is that we explore named entities and their coreference relations as important indicators for measuring the similarity. We conduct experiments and evaluate our proposed method on Microsoft Research Paraphrase Corpus. The experiment results show that named entities and their coreference relations improve significantly the performance of paraphrase identification and the proposed method is comparable with state-of-the-art methods for paraphrase identification.