Probabilistic learning approaches for indexing and retrieval with the TREC-2 collection
Norbert Fuhr, Ulrich Pfeifer, C. Bremkamp, Monique Maria Henriettte Pollmann, Chris Buckley · 1993
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. 1 Introduction The good TREC-1 results of our group described in [Fuhr & Buckley 93] have confirmed the general concept of probabilistic retrieval as a learning approach. In this paper, we describe some...