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

information retrieval Information retrieval (IR) in computing and information science is the process of obtaining information system resources that are relevant to an information need from a collection of those resources. Searches can be based on full-text or other co ...

Overview

This is a method similar to tf-idf but it deals with finding keywords suitable for

and ones that are not. Please refer to Vector Space Model first. This method uses the concept of ''Vector Space Density'' that the less dense an

occurrence matrix A document-term matrix is a mathematical matrix that describes the frequency of terms that occur in a collection of documents. In a document-term matrix, rows correspond to documents in the collection and columns correspond to terms. This matrix i ...

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 document-term matrix is a mathematical matrix that describes the frequency of terms that occur in a collection of documents. In a document-term matrix, rows correspond to documents in the collection and columns correspond to terms. This matrix i ...

A

and one keyword:

k

* Find the global document centroid:

C

(this is just the average document vector) * Find the average euclidean distance 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 Recall may refer to: * Recall (bugle call), a signal to stop * Recall (information retrieval), a statistical measure * ''ReCALL'' (journal), an academic journal about computer-assisted language learning * Recall (memory) * ''Recall'' (Overwatch ...

,'' whereas keywords that are ''frequent'' should be poor discriminators because they have poor '' precision.''

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