In information retrieval, it is common to model index terms and documents as vectore in a suitably defined vector space. The main di]ficulty with this approach is that the explicit repreeentation of term vectors is not known a priorL For th~ mason, the vector space model adopted by Salton for the SMART system treats the terms as a set of orthogonal vectom In such a model it is often necessary to adopt a separate, corrective procedure to take into account the correlations between terms. In this paper, we propose a systematic method (the generalized vector space model) to compute term correlations directly from automatic indexing scheme. We also demonstrate how such correlations can be included with minimal modification in the existing vector based information retrieval systems. The preliminary experimental . results obtained from the new model are very encouraging.

The Vector Space Model (VSM) has been adopted in information retrieval as a means of coping with inexact representation of documents and queries, and the resulting difficulties in determining the relevance of a document relative to a given query. The major problem in employing this approach is that the explicit representation of term vectors is not known a priori. Consequently, earlier researchers made the assumption that the vectors corresponding to terms are pairwise orthogonal. Such an assumption is clearly unrealistic. Although attempts have been made to compensate for this assumption by some separate, corrective steps, such methods are ad hoc and, in most cases, formally inconsistent. In this paper, a generalization of the VSM, called the GVSM, is advanced. The developments provide a solution not only for the computation of a measure of similarity (correlation) between terms, but also for the incorporation of these similarities into the retrieval process. The major strength of the GVSM derives from the fact that it is theoretically sound and elegant. Furthermore, experimental evaluation of the model on several test collections indicates that the performance is better than that of the VSM. Experiments have been performed on some variations of the GVSM, and all these results have also been compared to those of the VSM, based on inverse document frequency weighting. These results and some ideas for the efficient implementation of the GVSM are discussed.

An infamation retrieval model, named the Generaliied Vectm Spice Model (GVSM). is extended m handle situations where queries are specitied as (extended) Boolean expressions. It is shown tbat this unified model, unlike currently available alternatives, has the advantage of inwrpating tetm cortelations inm the retrieval process. 'Ilte query language extension is attractive in the sense that most of the aIgebraic properties of tbe strict Boolean language are still preserved. Although the experimental results for extended Boolean retrieval are not always better than the vector processing method, the developments here am signiecant in facilitating commercially available retrieval systems to benefit from the vector based methods. The proposed scheme is compared m the pnorm model advanced by Salmn snd coworkers. An important conclusion is that it is desirable m investigate further extensions that can offer the benefits of both proposals.

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