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COLING
2008

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

11 years 4 months ago
A Probabilistic Model for Measuring Grammaticality and Similarity of Automatically Generated Paraphrases of Predicate Phrases
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, Satoshi Sato
Added 24 Jan 2011
Updated 24 Jan 2011
Type Journal
Year 2008
Where COLING
Authors Atsushi Fujita, Satoshi Sato
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