probabilistic document indexing from relevance feedback data
Based on the binary independence indexing model, we apply three new concepts for probabilistic document indexing from relevance feedback data: Abstraction from specific terms and documents, which overcomes the restriction of limited relevance information for parameter estimation. Flexibility of the representation, which allows the integration of new text analysis and knowledge-based methods in our approach as well as the consideration of more complex document structures or different types of terms (e.g. single words and noun phrases). Probabilistic learning or classification methods for the estimation of the indexing weights making better use of the available relevance information. We give experimental results for five test collections which show improvements over other indexing methods.
documentation indexing relevance data model concept informal parameter estimation estimates representation integrability text analysis method complexity types learning classification use experimentation test collection relevance-feedback knowledge