Citation-Key:
Fuhr/etal:94
Title:
Probabilistic Learning Approaches for Indexing and Retrieval with the TREC-2 Collection
Author(s):
N. Fuhr
U. Pfeifer
C. Bremkamp
M. Pollmann
C. Buckley
In:
Citation-Key:
TREC-2
Title:
The Second Text REtrieval Conference (TREC-2)
Editor(s):
D. Harman
Publisher:
National Institute of Standards and Technology
In:
The Second Text REtrieval Conference (TREC-2)
Year:
1994

Classification(s):
H.3.3
General terms:
experimentation

BibTeX entry

Page(s):
67--74
Year:
1994

Abstract:
In this paper, we describe the application of probabilistic models for indexing and retrieval with the TREC-2 collection. This database consists of about a million documents (2 gigabytes of data) and 100 queries (50 routing and 50 adhoc topics). For document indexing, we use a description-oriented approach which exploits relevance feedback data in order to produce a probabilistic indexing with single terms as well as with phrases. With the adhoc queries, we present a new query term weighting method based on a training sample of other queries. For the routing queries, the RPI model is applied which combines probabilistic indexing with query term weighting based on query-specific feedback data. The experimental results of our approach show very good performance for both types of queries.
Classification(s):
H.3.3, G.1.2, H.3.1
Subject descriptor(s):
least squares approximation, indexing methods, retrieval models
Keywords:
query expansion, probabilistic models

BibTeX entry

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