tech,1-1-P01-1008,bq English grammar </term> are given . While <term> paraphrasing </term> is critical both for <term> interpretation
tech,6-1-P01-1008,bq paraphrasing </term> is critical both for <term> interpretation and generation of natural language </term> , current systems use manual or semi-automatic
other,22-1-P01-1008,bq semi-automatic methods to collect <term> paraphrases </term> . We present an <term> unsupervised
tech,3-2-P01-1008,bq <term> paraphrases </term> . We present an <term> unsupervised learning algorithm </term> for <term> identification of paraphrases
tech,7-2-P01-1008,bq unsupervised learning algorithm </term> for <term> identification of paraphrases </term> from a <term> corpus of multiple English
lr,12-2-P01-1008,bq identification of paraphrases </term> from a <term> corpus of multiple English translations </term> of the same <term> source text </term>
other,20-2-P01-1008,bq English translations </term> of the same <term> source text </term> . Our approach yields <term> phrasal
other,3-3-P01-1008,bq source text </term> . Our approach yields <term> phrasal and single word lexical paraphrases </term> as well as <term> syntactic paraphrases
other,12-3-P01-1008,bq lexical paraphrases </term> as well as <term> syntactic paraphrases </term> . This paper presents a <term> formal
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