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Learning to Find Answers to Questions on the Web EUGENE AGICHTEIN
 

Summary: Learning to Find Answers to Questions on the Web
EUGENE AGICHTEIN
Columbia University
STEVE LAWRENCE
NEC Research Institute
and
LUIS GRAVANO
Columbia University
We introduce a method for learning to find documents on the web that contain answers to a
given natural language question. In our approach, questions are transformed into new queries
aimed at maximizing the probability of retrieving answers from existing information retrieval
systems. The method involves automatically learning phrase features for classifying questions
into different types, automatically generating candidate query transformations from a training set
of question/answer pairs, and automatically evaluating the candidate transformations on target
information retrieval systems such as real-world general purpose search engines. At run time,
questions are transformed into a set of queries, and re-ranking is performed on the documents
retrieved. We present a prototype search engine, Tritus, that applies the method to web search
engines. Blind evaluation on a set of real queries from a web search engine log shows that the
method significantly outperforms the underlying search engines, and outperforms a commercial
search engine specializing in question answering. Our methodology cleanly supports combining

  

Source: Agichtein, Eugene - Department of Mathematics and Computer Science, Emory University

 

Collections: Computer Technologies and Information Sciences