document: Probabilistic question answering on the web Web-based search engines such as Google and NorthernLight return documents that are relevant to a user query , not answers to user questions . We have developed an architecture that augments existing search engines so that they support natural language question answering . The process entails five steps : query modulation , document retrieval , passage extraction , phrase extraction , and answer ranking . In this paper we describe some probabilistic approaches to the last three of these stages . We show how our techniques apply to a number of existing search engines and we also present results contrasting three different methods for question answering . Our algorithm , probabilistic phrase reranking ( PPR ) using proximity and question type features achieves a total reciprocal document rank of .20 on the TREC 8 corpus . Our techniques have been implemented as a Web-accessible system , called NSIR .
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