Extended Boolean query processing in the generalized vector space model

Abstract

An information retrieval model, named the Generalized Vector Space Model (GVSM), is extended to handle situations where queries are specified as weighted Boolean expressions. It is shown that this unified model, unlike currently available alternatives, has the advantage of incorporating term correlations into the retrieval process. The query language extension is attractive in the sense that most of the algebraic properties of the strict Boolean language are still preserved. Although the experimental results for the proposed extended Boolean retrieval are not always better than the vector processing method, the developments here are significant in facilitating commercially available retrieval systems to benefit from the vector based methods. It is shown that relevance feedback techniques can be employed in this extended Boolean environment and, for both document collections tested, significant improvements over the initial search are obtained after the modification of queries via feedback. The proposed scheme is compared to the p-norm model advanced by Salton and co-workers. An important conclusion is that it is desirable to investigate further extensions that can offer the benefits of both proposals.