Learning Paraphrases to Improve a Question-Answering System

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 implausible paraphrases are filtered out using an EM-based unsupervised clustering procedure. This learning machinery is built on top of an existing question-answering (QA) system and the learnt paraphrases will eventually be used to improve its recall. We focus here on the learning aspect of this system and report preliminary results.