Simple matching coefficient

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Cluster based retrieval
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Document representative
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E measure
Normalised association measures

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39 There are five commonly used measures of association in information retrieval ...The simplest of all association measures is X [[intersection]]Y Simple matching coefficient which is the number of shared index terms ...These may all be considered to be normalised versions of the simple matching coefficient ...then X 1 1 Y 1 1 X 1 [[intersection]]Y 2 1 >S 1 1 S 2 1 X 2 10 Y 2 10 X 2 [[intersection]]Y 2 1 >S 1 1 S 2 1 10 S 1 X 1,Y 1 S 1 X 2,Y 2 which is clearly absurd since X 1 and Y 1 are identical representatives whereas X 2 and Y 2 are radically different ...Doyle [17]hinted at the importance of normalisation in an amusing way:One would regard the postulate All documents are created equal as being a reasonable foundation for a library description ...
97 then to satisfy the K 1 AND K 2 part we intersect the K 1 and K 2 lists,to satisfy the K 3 AND NOT K 4 part we subtract the K 4 list from the K 3 list ...A slight modification of the full Boolean search is one which only allows AND logic but takes account of the actual number of terms the query has in common with a document ...For the same example as before with the query Q K 1 AND K 2 AND K 3 we obtain the following ranking:Co ordination level 3 D 1,D 2 2 D 3 1 D 4 In fact,simple matching may be viewed as using a primitive matching function ...Matching functions Many of the more sophisticated search strategies are implemented by means of a matching function ...There are many examples of matching functions in the literature ...If M is the matching function,D the set of keywords representing the document,and Q the set representing the query,then: