Retrieving documents by plausible inference: An experimental study

Croft, W. Bruce; Lucia, T. J.; Cringean, Janey K.; Willett, Peter

doi:10.1016/0306-4573(89)90095-2

Cited by 48 publications

(15 citation statements)

References 9 publications

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“…In some cases the same new book used as query may have its correct cluster ranked first by one method, and yet it would be placed much further down in the rankings by another method. This phenomena has also been observed in more conventional information retrieval tests (Croft et al, 1989).…”

Section: Discussionmentioning

confidence: 80%

Experiments in automatic Library of Congress Classification

Larson

1992

J. Am. Soc. Inf. Sci.

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This article presents the results of research into the automatic selection of Library of Congress Classification numbers based on the titles and subject headings in MARC records. The method used in this study was based on partial match retrieval techniques using various elements of new records (i.e., those to be classified) as "queries," and a test database of classification clusters generated from previously classified MARC records. Sixty individual methods for automatic classification were tested on a set of 283 new records, using all combinations of four different partial match methods, five query types, and three representations of search terms. The results indicate that if the best method for a particular case can be determined, then up to 88% of the new records may be correctly classified. The single method with the best accuracy was able to select the correct classification for about 46% of the new records.

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Section: Discussionmentioning

confidence: 80%

Experiments in automatic Library of Congress Classification

Larson

1992

J. Am. Soc. Inf. Sci.

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“…In some sense, our work resembles previous work on spreading activation [7,33,12,13,34,15,28,35,23,11] as both involve propagating values through a network/graph. The main difference, however, is that in these spreading activation methods, the number of steps for propagating the weights is predefined and is a small value in most of the cases, while our framework is an iterative process which iterates until the ranks converge to a limit.…”

Section: Introductionmentioning

confidence: 74%

A probabilistic relevance propagation model for hypertext retrieval

Shakery

Zhai

2006

Proceedings of the 15th ACM International Conference on Information and Knowledge Management - CIKM '06

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A major challenge in developing models for hypertext retrieval is to effectively combine content information with the link structure available in hypertext collections. Although several link-based ranking methods have been developed to improve retrieval results, none of them can fully exploit the discrimination power of contents as well as fully exploit all useful link structures. In this paper, we propose a general relevance propagation framework for combining content and link information. The framework gives a probabilistic score to each document defined based on a probabilistic surfing model. Two main characteristics of our framework are our probabilistic view on the relevance propagation model and propagation through multiple sets of neighbors. We compare eight different models derived from the probabilistic relevance propagation framework on two standard TREC Web test collections. Our results show that all the eight relevance propagation models can outperform the baseline content only ranking method for a wide range of parameter values, indicating that the relevance propagation framework provides a general, effective and robust way of exploiting link information. Our experiments also show that using multiple neighbor sets outperforms using just one type of neighbors significantly and taking a probabilistic view of propagation provides guidance on setting propagation parameters.

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“…When viewed as a diffusion algorithm, our work is also related to the many spreading activation algorithms (Belew 1989;Kwok 1989;Salton and Buckley 1988;Wilkinson and Hingston 1991;Croft et al 1988) and inference network techniques (Turtle and Croft 1990;Metzler and Croft 2004). In these systems, terms and documents form a bipartite graph.…”

Section: Spreading Activationmentioning

confidence: 98%

Regularizing query-based retrieval scores

Dı́az

2007

Inf Retrieval

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We adapt the cluster hypothesis for score-based information retrieval by claiming that closely related documents should have similar scores. Given a retrieval from an arbitrary system, we describe an algorithm which directly optimizes this objective by adjusting retrieval scores so that topically related documents receive similar scores. We refer to this process as score regularization. Because score regularization operates on retrieval scores, regardless of their origin, we can apply the technique to arbitrary initial retrieval rankings. Document rankings derived from regularized scores, when compared to rankings derived from un-regularized scores, consistently and significantly result in improved performance given a variety of baseline retrieval algorithms. We also present several proofs demonstrating that regularization generalizes methods such as pseudorelevance feedback, document expansion, and cluster-based retrieval. Because of these strong empirical and theoretical results, we argue for the adoption of score regularization as general design principle or post-processing step for information retrieval systems.

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Retrieving documents by plausible inference: An experimental study

Cited by 48 publications

References 9 publications

Experiments in automatic Library of Congress Classification

Experiments in automatic Library of Congress Classification

A probabilistic relevance propagation model for hypertext retrieval

Regularizing query-based retrieval scores

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