Abstract
Paraphrases are sentences or phrases that convey the same meaning using different words. Paraphrase recognition is of interest for many current Natural Language Processing (NLP) tasks. As understood in linguistics, thephenomenon ofparaphrases is difficult to characterize. In this article, we present a novel approach to the task of paraphrase identification. The proposed approach measures similarity between two sentences based on both the lexical and semantic levels, via combining neural networks and keywords jointly. In particular, we employ a vector offset, which implies the relation of given inputs in vector space, as the representation of a neural classifier. We conduct experiments on the Microsoft Research Paraphrase Corpus (MSRP)1 and SICK dataset, which are both standard datasets for evaluating approaches to paraphrase identification. The experiments showed that our proposed approach makes much progress and achieves state-of-the-art results.1https://www.microsoft.com/en-us/download/details.aspx?id=52398
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