Concepts
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The way we interpret this hypothesis is that a term in the query used by a user is likely to be there because it is a good discriminator and hence we are interested in its close associates
...Discrimination power of an index term On p
...and in fact there made the comment that it was a measure of the power of term i to discriminate between relevant and non relevant documents
...Instead of Ki I suggest using the information radius,defined in Chapter 3 on p
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nice property of being invariant under one to one transformations of the co ordinates
...A function very similar to the expected mutual information measure was suggested by Jardine and Sibson [2]specifically to measure dissimilarity between two classes of objects
...Here u and v are positive weights adding to unit
...P x P x w 1 P w 1 P x w 2 P w 2 x 0,1 P x wi P x wi P x i 1,2 we recover the expected mutual information measure I x,wi
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where [[rho]]...[[rho]]X,Y W 0 which implies using the expression for the partial correlation that [[rho]]X,Y [[rho]]X,W [[rho]]Y,W Since [[rho]]X,Y <1,[[rho]]X,W <1,[[rho]]Y,W <1 this in turn implies that under the hypothesis of conditional independence [[rho]]X,Y <[[rho]]X,W or [[rho]]Y,W Hence if W is a random variable representing relevance then thecorrelation between it and either index term is greater than the correlation between the index terms
...Qualitatively I shall try and generalise this to functions other than correlation coefficients,Linfott [27]defines a type of informational correlation measure by rij 1 exp 2 I xi,xj [1 2]0 <rij <1 or where I xi,xj is the now familiar expected mutual information measure
...I xi,xj <I xi,W or I xj,W,where I
...Discrimination Gain Hypothesis:Under the hypothesis ofconditional independence the statistical information contained in oneindex term about another is less than the information contained ineither index term about relevance
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