Paper: A Probabilistic Model for Measuring Grammaticality and Similarity of Automatically Generated Paraphrases of Predicate Phrases

ACL ID C08-1029
Title A Probabilistic Model for Measuring Grammaticality and Similarity of Automatically Generated Paraphrases of Predicate Phrases
Venue International Conference on Computational Linguistics
Session Main Conference
Year 2008
Authors

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 expres- sions; however, the results do not ensure full coverage of the various paraphrase phenomena. This paper focuses on pro- ductive paraphrases realized by general transformation patterns, and addresses the issues in generating instances of phrasal paraphrases with those patterns. Our prob- abilistic 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 distribu- tional similarity measure for estimating se- mantic equivalence and substitutability.