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If we think of a simple retrieval strategy as operating by matching on the descriptors,whether they be keyword names or class names,then expanding representatives in either of these ways will have the effect of increasing the number of matches between document and query,and hence tends to improve recall
...Recall is defined in the introduction
...Jones [41]has reported a large number of experiments using automatic keyword classifications and found that in general one obtained a better retrieval performance with the aid of automatic keyword classification than with the unclassified keywords alone
...Unfortunately,even here the evidence has not been conclusive
...The discussion of keyword classifications has by necessity been rather sketchy
...Normalisation It is probably useful at this stage to recapitulate and show how a number of levels of normalisation of text is involved in generating document representatives
...Index term weighting can also be thought of as a process of normalisation,if the weighting scheme takes into account the number of different index terms per document
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The SMART measures In 1966,Rocchio gave a derivation of two overall indices of merit based on recall and precision
...The first of these indices is normalised recall
...Normalised recall Rnorm is the area between the actual case and the worst as a proportion of the area between the best and the worst
...see Salton [23],page 285
...A convenient explicit form of normalised recall is:where N is the number of documents in the system and N n the area between the best and the worst case to see this substitute ri N i 1 in the formula for Ab Aa
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