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Abstract

Index modeling and computer simulation techniques are used to examine the influence of indexing frequency distributions, indexing exhaustivity distributions, and three weighting methods on hypothetical document spaces in a vector-based information retrieval (IR) system. The way documents are indexed plays an important role in retrieval. The authors demonstrate the influence of different indexing characteristics on document space density (DSD) changes and document space discriminative capacity for IR. Document environments that contain a relatively higher percentage of infrequently occurring terms provide lower density outcomes than do environments where a higher percentage of frequently occurring terms exists. Different indexing exhaustivity levels, however, have little influence on the document space densities. A weighting algorithm that favors higher weights for infrequently occurring terms results in the lowest overall document space densities, which allows documents to be more readily differentiated from one another. This in turn can positively influence IR. The authors also discuss the influence on outcomes using two methods of normalization of term weights (i.e., means and ranges) for the different weighting methods.

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Abstract

Wolfram and Zhang are interested in the effect of different indexing exhaustivity, by which they mean the number of terms chosen, and of different index term distributions and different term weighting methods on the resulting document cluster organization. The Distance Angle Retrieval Environment, DARE, which provides a two dimensional display of retrieved documents was used to represent the document clusters based upon a document's distance from the searcher's main interest, and on the angle formed by the document, a point representing a minor interest, and the point representing the main interest. If the centroid and the origin of the document space are assigned as major and minor points the average distance between documents and the centroid can be measured providing an indication of cluster organization. in the form of a size normalized similarity measure. Using 500 records from NTIS and nine models created by intersecting low, observed, and high exhaustivity levels (based upon a negative binomial distribution) with shallow, observed, and steep term distributions (based upon a Zipf distribution) simulation runs were preformed using inverse document frequency, inter-document term frequency, and inverse document frequency based upon both inter and intra-document frequencies. Low exhaustivity and shallow distributions result in a more dense document space and less effective retrieval. High exhaustivity and steeper distributions result in a more diffuse space.