Shortcomings of this method are first, that it underrates term co-occurrences if each term occurs commonly in the document collection (for instance, for the query “rods and cones of the eye”, co-occurrences of rods, cones, and eye may well characterize relevant documents, even though all three terms occur quite commonly in non-physiological contexts), and second, that relevant documents might have few occurrences of the query terms, while containing many semantically related terms. Some of the vector methods mentioned in connection with document clustering can be used to alleviate these shortcomings. We may reduce the dimensionality of the term-based vector space using LSA, obtaining a much smaller “concept space” in which many terms that tend to co-occur in documents will have been merged into the same dimensions (concept). Thus sharing of concepts, rather than sharing of specific terms, becomes the basis for measuring relevance.