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Abstract: We describe a method for probabilistic document indexing using relevance feedback data that has been collected from a set of queries. Our approach is based on three new concepts: (1) Abstraction from specific terms and documents, which overcomes the restriction of limited relevance information for parameter estimation. (2) Flexibility of the representation, which allows the integration of new text analysis and knowledge-based methods in our approach as well as the consideration of document ... (Update)
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BibTeX entry: (Update)
Fuhr, N.; Buckley, C. (1991). A Probabilistic Learning Approach for Document Indexing. ACM Transactions on Information Systems 9(3), pages 223--248. http://citeseer.ist.psu.edu/fuhr91probabilistic.html More
@article{ fuhr91probabilistic,
author = "Norbert Fuhr and Chris Buckley",
title = "A Probabilistic Learning Approach for Document Indexing",
journal = "Information Systems",
volume = "9",
number = "3",
pages = "223-248",
year = "1991",
url = "citeseer.ist.psu.edu/fuhr91probabilistic.html" }
Citations (may not include all citations):463 Term weighting approaches in automatic text retrieval (context) - Salton, Buckley - 1988 ACM
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37 Models for retrieval with probabilistic indexing (context) - Fuhr - 1989 ACM
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16 Synthesizing statistical knowledge from incomplete mixedmode.. (context) - Wong, Chiu - 1987
13 Optimum polynomial retrieval functions based on the probabil.. (context) - Fuhr - 1989
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