Article

Free access

Document classification using a finite mixture model

Authors:

Kenji YamanishiAuthors Info & Claims

ACL '98/EACL '98: Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics

Pages 39 - 47

https://doi.org/10.3115/976909.979623

Published: 07 July 1997 Publication History

Abstract

We propose a new method of classifying documents into categories. We define for each category a finite mixture model based on soft clustering of words. We treat the problem of classifying documents as that of conducting statistical hypothesis testing over finite mixture models, and employ the EM algorithm to efficiently estimate parameters in a finite mixture model. Experimental results indicate that our method outperforms existing methods.

References

[1]

Apte, Chidanand, Fred Damerau, and Sholom M. Weiss. 1994. Automated learning of decision rules for text categorization. ACM Tran. on Information Systems, 12(3):233--251.

Digital Library

[2]

Cohen, William W. and Yoram Singer. 1996. Context-sensitive learning methods for text categorization. Proc. of SIGIR'96.

Digital Library

[3]

Deerwester, Scott, Susan T. Dumais, George W. Furnas, Thomas K. Landauer, and Richard Harshman. 1990. Indexing by latent semantic analysis. Journ. of the American Society for Information Science, 41(6):391--407.

[4]

Dempster, A. P., N. M. Laird, and D. B. Rubin. 1977. Maximum likelihood from incomplete data via the em algorithm. Journ. of the Royal Statistical Society, Series B, 39(1):1--38.

[5]

Everitt, B. and D. Hand. 1981. Finite Mixture Distributions. London: Chapman and Hall.

[6]

Fuhr, Norbert. 1989. Models for retrieval with probabilistic indexing. Information Processing and Management, 25(1):55--72.

Digital Library

[7]

Gale, Williams. A. and Kenth W. Church. 1990. Poor estimates of context are worse than none. Proc. of the DARPA Speech and Natural Language Workshop, pages 283--287.

Digital Library

[8]

Guthrie, Louise, Elbert Walker, and Joe Guthrie. 1994. Document classification by machine: Theory and practice. Proc. of COLING'94, pages 1059--1063.

Digital Library

[9]

Helmbold, D., R. Schapire, Y. Siuger, and M. Warmuth. 1995. A comparison of new and old algorithm for a mixture estimation problem. Proc. of COLT'95, pages 61--68.

Digital Library

[10]

Jelinek, F. and R. I. Mercer. 1980. Interpolated estimation of markov source parameters from sparse data. Proc. of Workshop on Pattern Recognition in Practice, pages 381--402.

[11]

Lewis, David D. 1992. An evaluation of phrasal and clustered representations on a text categorization task. Proc. of SIGIR'92, pages 37--50.

Digital Library

[12]

Lewis, David D. and Marc Ringuette. 1994. A comparison of two learning algorithms for test categorization. Proc. of 3rd Annual Symposium on Document Analysis and Information Retrieval, pages 81--93.

[13]

Lewis, David D., Robert E. Schapire, James P. Callan, and Ron Papka. 1996. Training algorithms for linear text classifiers. Proc. of SIGIR'96.

Digital Library

[14]

Pereira, Fernando, Naftali Tishby, and Lillian Lee. 1993. Distributional clustering of english words. Proc. of ACL'93, pages 183--190.

Digital Library

[15]

Robertson, S. E. and K. Sparck Jones. 1976. Relevance weighting of search terms. Journ. of the American Society for Information Science, 27:129--146.

[16]

Salton, G. and M. J. McGill. 1983. Introduction to Modern Information Retrieval. New York: McGraw Hill.

Digital Library

[17]

Schutze, Hinrich, David A. Hull, and Jan O. Pedersen. 1995. A comparison of classifiers and document representations for the routing problem. Proc. of SIGIR'95.

Digital Library

[18]

Tanner, Martin A. and Wing Hung Wong. 1987. The calculation of posterior distributions by data augmentation. Journ. of the American Statistical Association, 82(398):528--540.

[19]

Wong, S. K. M. and Y. Y. Yao. 1989. A probability distribution model for information retrieval. Information Processing and Management, 25(1):39--53.

Digital Library

[20]

Yamanishi, Kenji. 1996. A randomized approximation of the mdl for stochastic models with hidden variables. Proc. of COLT'96, pages 99--109.

Digital Library

[21]

Yang, Yiming and Christoper G. Chute. 1994. An example-based mapping method for text categorization and retrieval. ACM Tran. on Information Systems, 12(3):252--277.

Digital Library

Cited By

Ko Y(2015)New feature weighting approaches for speech-act classificationPattern Recognition Letters10.1016/j.patrec.2014.08.01451:C(107-111)Online publication date: 1-Jan-2015
https://dl.acm.org/doi/10.1016/j.patrec.2014.08.014
Zhang L(2013)Contextual and active learning-based affect-sensing from virtual drama improvisationACM Transactions on Speech and Language Processing 10.1145/2407736.24077389:4(1-25)Online publication date: 30-Jan-2013
https://dl.acm.org/doi/10.1145/2407736.2407738
Lee JTatar DPedersen E(2012)Time, topic and trawlProceedings of the Designing Interactive Systems Conference10.1145/2317956.2317993(234-243)Online publication date: 11-Jun-2012
https://dl.acm.org/doi/10.1145/2317956.2317993
Show More Cited By

Document classification using a finite mixture model
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
      2. Unsupervised learning
    2. Machine learning approaches
2. Hardware
  1. Power and energy
    1. Power estimation and optimization

Recommendations

PARAFAC-Based Blind Identification of Underdetermined Mixtures Using Gaussian Mixture Model

This paper presents a novel algorithm, named GMM-PARAFAC, for blind identification of underdetermined instantaneous linear mixtures. The GMM-PARAFAC algorithm uses Gaussian mixture model (GMM) to model non-Gaussianity of the independent sources. We show ...
An Expectation-Maximization Algorithm for Blind Separation of Noisy Mixtures Using Gaussian Mixture Model

In this paper, we propose a new expectation-maximization (EM) algorithm, named GMM-EM, to blind separation of noisy instantaneous mixtures, in which the non-Gaussianity of independent sources is exploited by modeling their distribution using the ...
Overdetermined blind source separation by gaussian mixture model
ICIC'11: Proceedings of the 7th international conference on Advanced Intelligent Computing Theories and Applications: with aspects of artificial intelligence

The blind separation of overdetermined mixtures, i.e., the case where more sensors than sources are available is considered in this paper. The contrast function for overdetermined blind source separation problem is presented, together with its gradient. ...

Comments

Information & Contributors

Information

Published In

cover image DL Hosted proceedings

ACL '98/EACL '98: Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics

July 1997

543 pages

Program Chairs:
Philip R. Cohen
Oregon Graduate Institute
,
Wolfgang Wahlster
DFKI Saarbrücken, Germany

Sponsors

Directorate General XIII (European Commission)
Universidad Complutense de Madrid
Universidad Autónoma de Madrid
Universidad Nacional de Educación a Distancia
Universidad Politécnica de Madrid

Publisher

Association for Computational Linguistics

United States

Publication History

Published: 07 July 1997

Qualifiers

Article

Acceptance Rates

Overall Acceptance Rate 85 of 443 submissions, 19%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

11
Total Citations
View Citations
390
Total Downloads

Downloads (Last 12 months)32
Downloads (Last 6 weeks)13

Reflects downloads up to 10 Oct 2024

Other Metrics

View Author Metrics

Citations

Cited By

Ko Y(2015)New feature weighting approaches for speech-act classificationPattern Recognition Letters10.1016/j.patrec.2014.08.01451:C(107-111)Online publication date: 1-Jan-2015
https://dl.acm.org/doi/10.1016/j.patrec.2014.08.014
Zhang L(2013)Contextual and active learning-based affect-sensing from virtual drama improvisationACM Transactions on Speech and Language Processing 10.1145/2407736.24077389:4(1-25)Online publication date: 30-Jan-2013
https://dl.acm.org/doi/10.1145/2407736.2407738
Lee JTatar DPedersen E(2012)Time, topic and trawlProceedings of the Designing Interactive Systems Conference10.1145/2317956.2317993(234-243)Online publication date: 11-Jun-2012
https://dl.acm.org/doi/10.1145/2317956.2317993
Staff CBugeja IKraaij Wde Vries AClarke CFuhr NKando N(2007)Automatic classification of web pages into bookmark categoriesProceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval10.1145/1277741.1277881(731-732)Online publication date: 23-Jul-2007
https://dl.acm.org/doi/10.1145/1277741.1277881
Razek MFrasson CKaltenbach M(2003)Dominant meanings classification model for web informationDesign and application of hybrid intelligent systems10.5555/998038.998152(1044-1053)Online publication date: 1-Jan-2003
https://dl.acm.org/doi/10.5555/998038.998152
Krishnapuram RChitrapura KJoshi S(2003)Classification of text documents based on minimum system entropyProceedings of the Twentieth International Conference on International Conference on Machine Learning10.5555/3041838.3041887(384-391)Online publication date: 21-Aug-2003
https://dl.acm.org/doi/10.5555/3041838.3041887
Ko YSeo JKay M(2000)Automatic text categorization by unsupervised learningProceedings of the 18th conference on Computational linguistics - Volume 110.3115/990820.990886(453-459)Online publication date: 31-Jul-2000
https://dl.acm.org/doi/10.3115/990820.990886
Li HYamanishi KSchiitze HSu K(2000)Topic analysis using a finite mixture modelProceedings of the 2000 Joint SIGDAT conference on Empirical methods in natural language processing and very large corpora: held in conjunction with the 38th Annual Meeting of the Association for Computational Linguistics - Volume 1310.3115/1117794.1117799(35-44)Online publication date: 7-Oct-2000
https://dl.acm.org/doi/10.3115/1117794.1117799
Androutsopoulos IKoutsias JChandrinos KSpyropoulos CYannakoudakis EBelkin NLeong MIngwersen P(2000)An experimental comparison of naive Bayesian and keyword-based anti-spam filtering with personal e-mail messagesProceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval10.1145/345508.345569(160-167)Online publication date: 1-Jul-2000
https://dl.acm.org/doi/10.1145/345508.345569
Nigam KMcCallum AThrun SMitchell T(2000)Text Classification from Labeled and Unlabeled Documents using EMMachine Language10.1023/A:100769271308539:2-3(103-134)Online publication date: 1-May-2000
https://dl.acm.org/doi/10.1023/A%3A1007692713085
Show More Cited By

View Options

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

Media

Figures

Other

Tables

View Table of Contents