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Keyword discrimination

We can immediately use this vector space for something useful, as the source of yet another approach to the question of appropriate keyword weightings. Recall that in Figure (FOAref) our initial assumption was that each and every keyword was to be used as a dimension of the vector space. Now we ask: What would happen if we removed one of these keywords?

The first step is to extend the measure \mathname{Sim}(\mathbf{q},\mathbf{d}) \) of document-query similarity to measure inter-document similarities \( \mathname{Sim}(\mathbf{d}_{i},\mathbf{d}_{j}) \) as well. Then, for an arbitrary measure of document-document similarity (e.g., the inner product measure mentioned above), we consider all pairs of documents, and then the AVERAGE SIMILARITY across all of them. {Fortunately, it turns out that there is a more efficient way to compute average similarity than actually comparing all ${n}\choose{2}$ document pairs. First, define the CENTROID document to be the average document; i.e., the resulting of adding all\( \mathname{NDoc} \) vectors and dividing it by \( \mathname{NDoc} \). Then the average similarity can also be defined in terms of the distance of each document from this center. \overline{\mathname{Sim}_k}&\equiv&{1\over \mathname{NDoc}^2}\sum_{i,j} \mathname{Sim}(d_i, d_j) \\ &= &\alpha\sum_{i=1}^{\mathname{NDoc}} \mathname{Sim}(d_i, D^*) }

Recall that our goal is to devise a representation of documents that makes it easiest for queries to discriminate them. Since each keyword corresponds to a dimension, {removing} one results in a {\em compression} of the space into $K-1$ dimensions, and we can expect that the representation of each of the documents will change at least slightly. More precisely, removing a dimension along which the documents varied significantly means that vectors which were far apart in the $K$-dimensional space are now much closer together.

This observation can be used to ask just how useful each potential keyword is. If it is discriminating, removing it will result in a signficant compression of the documents' vectors; if removing it changes very little, the keyword is less helpful. Using the average similarity as our measure of just how close together the documents are, and asking this question for each and every keyword, we arrive at yet another measure of keyword discrimination:

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