Term discrimination is a way to rank keywords in how useful they are for information retrieval.

Overview

This is a method similar to tf-idf but it deals with finding keywords suitable for information retrieval and ones that are not. Please refer to

Vector Space Model Vector space model or term vector model is an algebraic model for representing text documents (and any objects, in general) as vectors of identifiers (such as index terms). It is used in information filtering, information retrieval, indexing and ...

first. This method uses the concept of ''Vector Space Density'' that the less dense an occurrence matrix is, the better an information retrieval query will be. An optimal index term is one that can distinguish two different documents from each other and relate two similar documents. On the other hand, a sub-optimal index term can not distinguish two different document from two similar documents. The discrimination value is the difference in the occurrence matrix's vector-space density versus the same matrix's vector-space without the index term's density. Let:

A

be the occurrence matrix

A_k

be the occurrence matrix without the index term

k

and

Q(A)

be density of

A

. Then: The discrimination value of the index term

k

is:

DV_k = Q(A) - Q(A_k)

How to compute

Given an occurrency matrix:

A

and one keyword:

k

* Find the global document

centroid In mathematics and physics, the centroid, also known as geometric center or center of figure, of a plane figure or solid figure is the arithmetic mean position of all the points in the surface of the figure. The same definition extends to any ...

C

(this is just the average document vector) * Find the average

euclidean distance In mathematics, the Euclidean distance between two points in Euclidean space is the length of a line segment between the two points. It can be calculated from the Cartesian coordinates of the points using the Pythagorean theorem, therefor ...

from every document vector,

D_i

C

* Find the average euclidean distance from every document vector,

D_i

C

''IGNORING''

k

* The difference between the two values in the above step is the ''discrimination value'' for keyword

K

A higher value is better because including the keyword will result in better information retrieval.

Qualitative Observations

Keywords that are '' sparse'' should be poor discriminators because they have poor '' recall,'' whereas keywords that are ''frequent'' should be poor discriminators because they have poor ''

precision Precision, precise or precisely may refer to: Science, and technology, and mathematics Mathematics and computing (general) * Accuracy and precision, measurement deviation from true value and its scatter * Significant figures, the number of digit ...

.''

References

* G. Salton, A. Wong, and C. S. Yang (1975),
A Vector Space Model for Automatic Indexing
" ''Communications of the ACM'', vol. 18, nr. 11, pages 613–620. ''(The article in which the vector space model was first presented)'' * Can, F., Ozkarahan, E. A (1987), "Computation of term/document discrimination values by use of the cover coefficient concept." ''Journal of the American Society for Information Science'', vol. 38, nr. 3, pages 171-183. Information retrieval techniques