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By Gerald Salton

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A. 0000 A :> B T me N1KD i-lest 14 B. Standard TF:f\ A. 0084 A :> B A ;> B 23 % 8% To summarize, several methods based on the multiplication of standard term frequency weights by inverse document frequency and discrimination values have been found that appear to offer high performance standards. Among the methods which offer statistically significant improvements over the standard term weighting procedures for all processing environments, the following are the most promising: (a) ft standard weights with elimination of poor discriminators; (b) /* • WFk without elimination, or with elimination of poor discriminators or of terms with high document frequency; (c) fkt-DVk with elimination of poor discriminators or of high frequency terms.

Recall-precision results averaged for 1,033 document abstracts and 35 user queries are shown for the system in Fig. 9. A recall-precision graph such as the one in Fig. 9 is simply a graphic representation of the standard recall-precision tables in which adjacent precision values are joined by a line. 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.

Standard f\ run vs. B. PT phrases from nondiscriminators A. Standard /* run vs. B. SPT phrases from discriminators A. Standard /J run vs. B. Combined PT + SPT phrases A. ft • IDF weights vs. B. 01 (A> B) a thesaurus class, the class will exhibit a much higher document frequency, and most likely a better discrimination value, than any of the original terms. There exist well-known procedures for constructing thesauruses either manually or automatically [10], [12], [24]. In the latter case, automatic term classification methods may be used to generate the appropriate term groups [30].

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A Theory of indexing by Gerald Salton


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