In this paper, we present a nearly unsupervised learning methodology for automatically extracting paraphrases from the Web. Starting with one single linguistic expression of a semantic relationship, our learning algorithm repeatedly samples the Web, in order to build a corpus of potential new examples of the same relationship. Sampling steps alternate with validation steps, during which implaus...

Paraphrase identification is a core Natural Language Processing task that involves assessing the semantic similarity of two texts. To foster systematic studies of this task, standardized datasets were created on which various approaches could be compared more fairly. However, a better understanding and more precise operational definition of a paraphrase are needed before any further datasets or...

Automatic Machine Translation (MT) evaluation metrics have traditionally been evaluated by the correlation of the scores they assign to MT output with human judgments of translation performance. Different types of human judgments, such as Fluency, Adequacy, and HTER, measure varying aspects of MT performance that can be captured by automatic MT metrics. We explore these differences through the ...

We focus on paraphrases for information extraction: expressions which should produce the same extraction output. These expressions are acquired automatically from comparable news articles (articles from the same day, on the same topic). Candidate paraphrases are paths in predicate argument structure starting from matching anchors (typically, names) in the two sentences. By using such syntactica...

We describe an automatic paraphrase-inference procedure for a highly inflected language like Arabic. Paraphrases are derived from comparable documents, that is, distinct documents dealing with the same topic. A co-training approach is taken, with two classifiers, one designed to model the contexts surrounding occurrences of paraphrases, and the other trained to identify significant features of ...

This paper describes a method for checking the acceptability of paraphrases in context. We use the Google n-gram data and a CCG parser to certify the paraphrasing grammaticality and fluency. We collect a corpus of human judgements to evaluate our system. The ultimate goal of our work is to integrate text paraphrasing into a Linguistic Steganography system, by using paraphrases to hide informati...