Abstract
The paper deals with the problem of automatic stop list generation for processing recognition results of call center recordings, in particular for the purpose of clustering. We propose and test a supervised domain dependent method of automatic stop list generation. The method is based on finding words whose removal increases the dissimilarity between documents in different clusters, and decreases dissimilarity between documents within the same cluster. This approach is shown to be efficient for clustering recognition results of recordings with different quality, both on datasets that contain the same topics as the training dataset, and on datasets containing other topics.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Zipf, K.: Selective Studies and the Principle of Relative Frequency in Language. MIT Press, Cambridge (1932)
Dragut, E., Fang, F., Sistla, P., Yu, C., Meng, W.: Stop Word and Related Problems in Web Interface Integration. In: 35th International Conference on Very Large Data Bases (VLDB 2009), Lyon, France, pp. 349–360 (2009)
Yang, Y.: Noise Reduction in a Statistical Approach to Text Categorization (pdf). In: Proc of the SIGIR 1995, pp. 256–263 (1995)
Popova, S., Kovriguina, L., Mouromtsev, D., Khodyrev, I.: Stop-words in keyphrase extraction problem. In: Open Innovations Association (FRUCT), pp. 131–121 (2013)
Popova, S., Khodyrev, I., Ponomareva, I., Krivosheeva, T.: Automatic speech recognition texts clustering. In: Sojka, P. (ed.) TSD 2014. LNCS (LNAI), vol. 8655, pp. 489–498. Springer, Heidelberg (2014)
Korenevsky, M., Bulusheva, A., Levin, K.: Unknown Words Modeling in Training and Using Language Models for Russian LVCSR System. In: Proc. of the SPECOM 2014, Kazan, Russia (2011)
Tomashenko, N.A., Khokhlov, Y.Y.: Fast Algorithm for Automatic Alignment of Speech and Imperfect Text Data. In: Železný, M., Habernal, I., Ronzhin, A. (eds.) SPECOM 2013. LNCS (LNAI), vol. 8113, pp. 146–153. Springer, Heidelberg (2013)
Schwarz, P.: Phoneme recognition based on long temporal context, Doctoral thesis, Brno, Brno University of Technology, Faculty of Information Technology (2008)
MacQueen, J.B.: Some Methods for classification and Analysis of Multivariate Observations. In: Proc. of 5th Berkeley Symposium on Mathematical Statistics and Probability 1, pp. 281–297. University of California Press, Berkeley (1967)
Meyer zu Eissen, S., Stein, B.: Analysis of Clustering Algorithms for Web-based Search. In: Karagiannis, D., Reimer, U. (eds.) PAKM 2002. LNCS (LNAI), vol. 2569, pp. 168–178. Springer, Heidelberg (2002)
Stein, B., Meyer zu Eissen, S., Wilbrock, F.: On Cluster Validity and the Information Need of Users. In: Hanza, M.H. (ed.) 3rd IASTED Int. Conference on Artificial Intelligence and Applications (AIA 2003), Benalmadena, Spain, pp. 216–221. ACTA Press, IASTED (2003) ISBN 0-88986-390-3
Rousseeuw, P.J.: Silhouettes: A Graphical Aid to the Interpretation and Validation of Cluster Analysis. Computational and Applied Mathematics 20, 53–65 (1987)
Cagnina, L., Errecalde, M., Ingaramo, D., Rosso, P.: A discrete particle swarm optimizer for clustering short text corpora. In: BIOMA 2008, pp. 93–103 (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Popova, S., Krivosheeva, T., Korenevsky, M. (2014). Automatic Stop List Generation for Clustering Recognition Results of Call Center Recordings. In: Ronzhin, A., Potapova, R., Delic, V. (eds) Speech and Computer. SPECOM 2014. Lecture Notes in Computer Science(), vol 8773. Springer, Cham. https://doi.org/10.1007/978-3-319-11581-8_17
Download citation
DOI: https://doi.org/10.1007/978-3-319-11581-8_17
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11580-1
Online ISBN: 978-3-319-11581-8
eBook Packages: Computer ScienceComputer Science (R0)