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16th International Conference on Pattern Recognition (ICPR'02) - Volume 4
The Performance Analysis of a Chi-square Similarity Measure for Topic Related Clustering of Noisy Transcripts
Quebec City, QC, Canada
August 11-August 15
ISBN: 0-7695-1695-X
ASCII Text
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Oktay lbrahimov, lshwar Sethi, Nevenka Dimitrova, "The Performance Analysis of a Chi-square Similarity Measure for Topic Related Clustering of Noisy Transcripts," Pattern Recognition, International Conference on, vol. 4, pp. 40285, 16th International Conference on Pattern Recognition (ICPR'02) - Volume 4, 2002.
 
 
BibTex
x
 
@article{ 10.1109/ICPR.2002.1047452,
author = {Oktay lbrahimov and lshwar Sethi and Nevenka Dimitrova},
title = {The Performance Analysis of a Chi-square Similarity Measure for Topic Related Clustering of Noisy Transcripts},
journal ={Pattern Recognition, International Conference on},
volume = {4},
year = {2002},
issn = {1051-4651},
pages = {40285},
doi = {http://doi.ieeecomputersociety.org/10.1109/ICPR.2002.1047452},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - CONF
JO - Pattern Recognition, International Conference on
TI - The Performance Analysis of a Chi-square Similarity Measure for Topic Related Clustering of Noisy Transcripts
SN - 1051-4651
SP
EP
A1 - Oktay lbrahimov,
A1 - lshwar Sethi,
A1 - Nevenka Dimitrova,
PY - 2002
KW - null
VL - 4
JA - Pattern Recognition, International Conference on
ER -
 
Oktay lbrahimov, Oakland University
lshwar Sethi, Oakland University
Nevenka Dimitrova, Philips Research

The goal of the paper is to present a novel Chi-square similarity measure and assess its performance through comparison with well-known similarity measures such as Cosine, Dice, and Jaccard.

The Chi-square similarity measure has been designed to withstand the imperfections of transcribed spoken documents. The major difference of our similarity measure from others consists in the fact that in addition to searching for co-occurring words in documents, we also match informative closeness of common words. We assume that co-occurring words, which had been employed to convey the same information, should have the compatible significance in matching documents. To test it we apply the Chi-Square method.

Experimental results obtained via using an archive of transcribed news broadcasts demonstrate the high efficacy of the proposed methodology.

Citation:
Oktay lbrahimov, lshwar Sethi, Nevenka Dimitrova, "The Performance Analysis of a Chi-square Similarity Measure for Topic Related Clustering of Noisy Transcripts," icpr, vol. 4, pp.40285, 16th International Conference on Pattern Recognition (ICPR'02) - Volume 4, 2002
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