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Hierarchical Passage Retrieval

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Much recent research in information retrieval has concentrated on approaches which make use of language models to score documents. Typically, language models are constructed from the collection and relevance is defined in terms of the probability of the query string under these models. In particular, use of hierarchical Dirichlet models of the whole collection can be shown to naturally provide many desirable features, including term weighting similar to the well known tf.idf scheme.

In this talk I will describe the extension of this model to include the notion of a further subdivision of documents into passages. This model can be used to score documents while at the same time provides an indication of parts of the documents which are particularly relevant. A simple application of this model is the construction of summary information which can be displayed alongside the search results, although there are many other cases in which this information is of use. I shall present a comparison of this model with alternate approaches using standard test sets.

This talk is part of the Inference Group series.

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