Optimizing Document Indexing and Search Term Weighting Based on Probabilistic Models

We describe the application of probabilistic indexing and retrieval methods to the TREC material. For document indexing, we apply a description-oriented approach which uses relevance feedback information from previous queries run on the same collection. This method is also very exible w.r.t. the underlying document representation. In our experiments, we consider single words and phrases and use polynomial functions for mapping the statistical parameters of these terms onto probabilistic indexing weights. Based on these weights, a linear (utility-theoretic) retrieval function is applied when no relevance feedback data is available for the speciic query. Otherwise, the retrieval-with-probabilistic-indexing model can be used. The experimental results show excellent performance in both cases, but also indicate possible improvements.