A probabilistic model for measuring grammaticality and similarity of automatically generated paraphrases of predicate phrases

Abstract

The most critical issue in generating and recognizing paraphrases is development of wide-coverage paraphrase knowledge. Previous work on paraphrase acquisition has collected lexicalized pairs of expressions; however, the results do not ensure full coverage of the various paraphrase phenomena. This paper focuses on productive paraphrases realized by general transformation patterns, and addresses the issues in generating instances of phrasal paraphrases with those patterns. Our probabilistic model computes how two phrases are likely to be correct paraphrases. The model consists of two components: (i) a structured N-gram language model that ensures grammaticality and (ii) a distributional similarity measure for estimating semantic equivalence and substitutability © Atsushi Fujita and Satoshi Sato, 2008.