Questions

The web is now becoming one of the largest information and knowl-edge repositories. Many large scale search engines (Google, Fast, Northern Light, etc.) have emerged to help users find information. In this paper, we study how we can effectively use these existing search engines to mine the Web and discover the “correct ” answers to factual natural language questions. We propose a probabilistic algorithm calledQASM (Question An-swering using Statistical Models) that learns the best query para-phrase of a natural language question. We validate our approach for both local and web search engines using questions from the TREC evaluation. We also show how this algorithm can be com-bined with another algorithm (AnSel) to produce precise answers to natural language questions. 1