Probabilistic Information Retrieval as Combination of Abstraction, Inductive Learning and Probabilistic Assumptions
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- Citation-Key:
- Fuhr/Pfeifer:94
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- Title:
- Probabilistic Information Retrieval as Combination of Abstraction, Inductive Learning and Probabilistic Assumptions
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- Author(s):
- N. Fuhr
- U. Pfeifer
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- Journal:
- ACM Transactions on Information Systems
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- Volume:
- 12
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- Number:
- 1
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- Page(s):
- 92--115
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- Year:
- 1994
- Classification(s):
- H.3.3
- Subject descriptor(s):
- retrieval models
- Keywords:
- probabilistic retrieval
Abstract:
We show that former approaches in probabilistic information retrieval are based on one or two of the three concepts abstraction, inductive learning and probabilistic assumptions, and we propose a new approach which combines all three concepts. This approach is illustrated for the case of indexing with a controlled vocabulary. For this purpose, we describe a new probabilistic model first, which is then combined with logistic regression, thus yielding a generalization of the original model. Experimental results for the pure theoretical model as well as for heuristic variants are given. Furthermore, linear and logistic regression are compared.
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