Probabilistic Information Retrieval as Combination of Abstraction, Inductive Learning and Probabilistic Assumptions

  • Citation-Key:
    Fuhr/Pfeifer:94
  • Title:
    Probabilistic Information Retrieval as Combination of Abstraction, Inductive Learning and Probabilistic Assumptions
  • Author(s):
    N. Fuhr
    U. Pfeifer
  • Journal:
    ACM Transactions on Information Systems
  • Volume:
    12
  • Number:
    1
  • Page(s):
    92--115
  • Year:
    1994

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.
Classification(s):
H.3.3
Subject descriptor(s):
retrieval models
Keywords:
probabilistic retrieval
BibTeX entry

Fulltext as PS