Information Retrieval as Statistical Translation

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Information Retrieval as Statistical Translation
We propose a new probabilistic approach to information retrieval based upon the ideas and methods of statistical machine translation. The central ingredient in this approach is a statistical model of how a user might distill or \translate" a given document into a query. To assess the relevance of a document to a user's query, we estimate the probability that the query would have been generated as a translation of the document, and factor in the user's general preferences in the form of a prior distribution over documents. We propose a simple, well motivated model of the document-to-query translation process, and describe an algorithm for learning the parameters of this model in an unsupervised manner from a collection of documents. As we show, one can view this approach as a generalization and justi cation of the \language modeling" strategy recently proposed by Ponte and Croft. In a series of experiments on TREC data, a simple translation-based retrieval system pe...
Adam L. Berger, John D. Lafferty
Added 03 Aug 2010
Updated 03 Aug 2010
Type Conference
Year 1999
Authors Adam L. Berger, John D. Lafferty
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