This paper improves an existing bilingual paraphrase extraction technique using monolingual distributional similarity to rerank candidate paraphrases. Raw monolingual data provides a complementary and orthogonal source of information that lessens the commonly observed errors in bilingual pivotbased methods. Our experiments reveal that monolingual scoring of bilingually extracted paraphrases has a significantly stronger correlation with human judgment for grammaticality than the probabilities assigned by the bilingual pivoting method does. The results also show that monolingual distribution similarity can serve as a threshold for high precision paraphrase selection.
REF use monolingual distributional similarity to rank paraphrases generated by the syntactically-constrained pivot method.
Reranking Bilingually Extracted Paraphrases Using Monolingual Distributional Similarity
Proceedings of the GEMS 2011 Workshop on GEometrical Models of Natural Language Semantics
Mathematics