Information Retrieval as Statistical Translation (1999) [165 citations — 7 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 comparison to conventional retrieval techniques. This prototype system only begins to tap the full potential of translation-based retrieval. 1
