Most previous work on the recently developed languagemodeling approach to information retrieval focuses on document-specific characteristics, and therefore does not take into account the structure of the surrounding corpus. We propose a novel algorithmic framework in which information provided by document-based language models is enhanced by the incorporation of information drawn from clusters of similar documents. Using this framework, we develop a suite of new algorithms. Even the simplest typically outperforms the standard language-modeling approach in precision and recall, and our new interpolation algorithm posts statistically significant improvements for both metrics over all three corpora tested.
Predicting query performance, that is, the effectiveness of a search performed in response to a query, is a highly important and challenging problem. Our novel approach to addressing this challenge is based on estimating the potential amount of query drift in the result list, i.e., the presence (and dominance) of aspects or topics not related to the query in top-retrieved documents. We argue that query-drift can potentially be estimated by measuring the diversity (e.g., standard deviation) of the retrieval scores of these documents. Empirical evaluation demonstrates the prediction effectiveness of our approach for several retrieval models. Specifically, the prediction success is better, over most tested TREC corpora, than that of state-of-the-art prediction methods.
We present a novel framework for the query-performance prediction task. That is, estimating the effectiveness of a search performed in response to a query in lack of relevance judgments. Our approach is based on using statistical decision theory for estimating the utility that a document ranking provides with respect to an information need expressed by the query. To address the uncertainty in inferring the information need, we estimate utility by the expected similarity between the given ranking and those induced by relevance models; the impact of a relevance model is based on its presumed representativeness of the information need. Specific query-performance predictors instantiated from the framework substantially outperform state-of-the-art predictors over five TREC corpora.
Abstract. We show that several previously proposed passage-based document ranking principles, along with some new ones, can be derived from the same probabilistic model. We use language models to instantiate specific algorithms, and propose a passage language model that integrates information from the ambient document to an extent controlled by the estimated document homogeneity. Several document-homogeneity measures that we propose yield passage language models that are more effective than the standard passage model for basic document retrieval and for constructing and utilizing passage-based relevance models; the latter outperform a document-based relevance model. We also show that the homogeneity measures are effective means for integrating documentquery and passage-query similarity information for document retrieval.
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