A probabilistic method for computing term-by-term relationships

This article suggests a probabilistic method to compute the term relationships from relevance information, which complements the studies on a non‐probabilistic technique called pseudo‐classification. A quadratic ranking function (i.e., a bilinear function) on the components of document and query vec...

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Bibliographic Details
Published in:Journal of the American Society for Information Science 1993-09, Vol.44 (8), p.431-439
Main Authors: Wong, S. K. M., Yao, Y. Y.
Format: Article
Language:English
Subjects:
Algorithms
Associations
Automatic indexing
Automatic text analysis
Estimation (Mathematics)
Exact sciences and technology
Indexing
Information and communication sciences
Information processing and retrieval
Information retrieval
Information retrieval systems. Information and document management system
Information science. Documentation
Methods
Neural networks
Parameter estimation
Probabilistic methods
Probabilistic retrieval
Probability
Pseudo classification
Queries
Query formulation
Raghavan, V
Ranking
Relevance
Relevance (Information Retrieval)
Retrieval performance measures
Sciences and techniques of general use
Semantics
Statistical analysis
Vector space
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Summary:This article suggests a probabilistic method to compute the term relationships from relevance information, which complements the studies on a non‐probabilistic technique called pseudo‐classification. A quadratic ranking function (i.e., a bilinear function) on the components of document and query vectors is derived by incorporating the term‐by‐term relationships. The conventional probabilistic indexing model, the probabilistic retrieval model, and our earlier generalized model are special cases of the proposed model. By exploring the different views of probability, procedures for estimating the required parameters are provided. © 1993 John Wiley & Sons, Inc.
ISSN:0002-8231
1097-4571
DOI:10.1002/(SICI)1097-4571(199309)44:8<431::AID-ASI1>3.0.CO;2-V