A Theory of indexing by Gerald Salton PDF

By Gerald Salton

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B. Term freq. weights /* B. Term freq. with IDF B. Term freq. with IDF (/? 0000 Table 10 contains t-test and Wilcoxon signed rank test values, giving in each case the probability that the output results for the two test runs could have been generated from the same distribution of values. 05—indicate that the answer to this question is negative and that the test results are significantly different [24]. It may be seen in Table 10 that only 30 G. SALTON for the Time collection is there a significant difference between binary and term frequency weighting, with the latter being substantially better than the former (B > A).

FIG. 9. Performance of term deletion algorithm of Fig. 8; averages over 1033 documents and 35 queries (adapted from [25]). Additional reductions in the indexing vocabulary may be effected by further deletion of terms in increasing term value order. Thus the 5,941 terms constituting the A5 word list of Fig. 8 might be reduced to only 1,000 terms by deleting the 4,941 terms that exhibit the next lowest discrimination values. The recall-precision output of Fig. 10 reflects the retrieval performance for the previously used collection of 1,033 items in biomedicine, again averaged over 35 search requests.

The curve closest to the upper-right-hand corner of the graph (where recall and precision are highest) reflects the best performance. It may be seen in Fig. 9 that the deletion of frequencyone terms and of terms with large document frequencies produces substantial increases in the average recall and precision values. FIG. 9. Performance of term deletion algorithm of Fig. 8; averages over 1033 documents and 35 queries (adapted from [25]). Additional reductions in the indexing vocabulary may be effected by further deletion of terms in increasing term value order.

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A Theory of indexing by Gerald Salton

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