Information Retrieval as Statistical Translation

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Information Retrieval as Statistical Translation (1999)

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by Adam Berger , John Lafferty

Venue:	In Proceedings of the 1999 ACM SIGIR Conference on Research and Development in Information Retrieval
Citations:	312 - 6 self

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@INPROCEEDINGS{Berger99informationretrieval,
    author = {Adam Berger and John Lafferty},
    title = {Information Retrieval as Statistical Translation},
    booktitle = {In Proceedings of the 1999 ACM SIGIR Conference on Research and Development in Information Retrieval},
    year = {1999},
    pages = {222--229}
}

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Abstract

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 justification 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 performs well in compa...

Keyphrases

information retrieval statistical translation motivated model unsupervised manner statistical machine translation prior distribution simple translation-based retrieval system trec data document-to-query translation process new probabilistic approach general preference statistical model central ingredient

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