Search (6 results, page 1 of 1)

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Abstract

This paper proposes a new unified probabilistic model. Two previous models, Robertson et al.'s "Model 0" and "Model 3," each have strengths and weaknesses. The strength of Model 0 not found in Model 3, is that it does not require relevance data about the particular document or query, and, related to that, its probability estimates are straightforward. The strength of Model 3 not found in Model 0 is that it can utilize feedback information about the particular document and query in question. In this paper we introduce a new unified probabilistic model that combines these strengths: the expression of its probabilities is straightforward, it does not require that data must be available for the particular document or query in question, but it can utilize such specific data if it is available. The model is one way to resolve the difficulty of combining two marginal views in probabilistic retrieval.

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Abstract

We present a preliminary empirical test of a maximum likelihood approach to using relevance data for training information retrieval (IR) parameters. Similar to language models, our method uses explicitly hypothesized distributions for documents and queries, but we add to this an explicitly hypothesized distribution for relevance judgments. The method unifies document-oriented and query-oriented views. Performance is better than the Rocchio heuristic for document and/or query modification. The maximum likelihood methodology also motivates a heuristic estimate of the MLE optimization. The method can be used to test competing hypotheses regarding the processes of authors' term selection, searchers' term selection, and assessors' relevancy judgments.

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Abstract

2 competing approaches for document retrieval were first identified by Robertson, Maron and Cooper (1982) for probabilistic retrieval. The difficulty of unifying those approaches was introduced as a problem of resolving query-focused with document-focused retrieval, and an approach towards unification was offered. That approach rests on a re-conceptualization of the meaning of terms weight estimates. In this work, we propose a new unified model. The unification problem is re-framed as resulting from a lack of theory regarding the relationship to 2 sorts of data, absolute and relative. This new unified model is valid even for traditional interpretations of term estimates

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Abstract

The view of documents and/or queries as random variables is gaining importance in the theory of information retrieval. We argue that traditional probabilistic models consider documents and queries as random variables, but that newer models such as language modeling and our unified model take this one step further. The additional step is called error in predictors. Such models consider that we don't observe the document and query random variables that are modeled to predict relevance probabilistically. Rather, there are additional random variables, which are the observed documents and queries. We discuss some important implications of this idea for parameter estimation, relevance prediction, and even test-collection construction. By clarifying the positions of various probabilistic models on this question, and presenting in one place many of its implications, this article aims to deepen our common understanding of the theories behind traditional probabilistic models, and to strengthen the theoretical basis for further development of more recent approaches such as language modeling.

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Abstract

In the traditional model of information retrieval, searchers and indexers choose query and index terms, respectively, and these term choices are ultimately compared in a matching process. One of the main challenges in information science and information retrieval is that searchers and indexers often do not choose the same term even though the item is relevant to the need whereas at other times they do choose the same term even though it is not relevant. But if both searchers and indexers have the opportunity to review feedback data showing the success or failure of their previous term choices, then there exists an evolutionary force that, all else being equal, will lead to helpful convergence in searchers' and indexers' term usage when the information is relevant, and helpful divergence of term usage when it is not. Based on learning theory, and new theory presented here, it is possible to predict which terms will emerge as the terminological conventions that are used by groups of searchers and the indexers of relevant and nonrelevant information items.

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Date

22. 1.2016 11:58:25