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...

• Type: Conference
• Year: 1999
• Where: SIGIR
• Authors: Adam L. Berger, John D. Lafferty
• Keywords: Information Management, SIGIR 1999, Statistical Machine Translation, Translation-based Retrieval, User's General Preferences