Article

Free access

Distributional clustering of words for text classification

Authors:

Andrew Kachites McCallumAuthors Info & Claims

SIGIR '98: Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval

Pages 96 - 103

https://doi.org/10.1145/290941.290970

Published: 01 August 1998 Publication History

PDF eReader

References

[1]

P. F. Brown, P. V. deSouza, R. L. Mercer, V. J. Della Pietra, and J. C. Lai. Class-based n-gram models of natural language. Computational Linguistics, 18(4):467-479, 1992.

Digital Library

Google Scholar

[2]

Thomas Cover and Peter Hart. Nearest neighbor pattern classification. IEEE Transactions on Information Theory, 13(1):21-27, 1967.

Digital Library

Google Scholar

[3]

Thomas M. Cover and Joy A. Thomas. Elements of Information Theory. john Wiley, 1991.

Digital Library

Google Scholar

[4]

Mark Craven, Daniel DiPasquo, Dayne Freitag, Andrew McCallum, Tom Mitchell, Kamal Nigam, and Sean Slattery. Learning to extract symbolic knowledge from the World Wide Web. In Proceedings of the Fifteenth National Conference on Artificial Intelligence (AAAI-98), 1998.

Digital Library

Google Scholar

[5]

Ido Dagan, Fernando Pereira, and Lillian Lee. Similarity-based estimation of word cooccurrence probabilities. In Proceedings of the 32rid Annual Meeting of the Association .for Computational Linguistics, 1994.

Digital Library

Google Scholar

[6]

S. C. Deerwester, S. T. Dumais, T. K. Landaner, G. W. Furnas, and R. A. Harshman. Indexing by latent semantic analysis. Journal of the American Society for Information Science, 41(6):391-407, 1990.

Crossref

Google Scholar

[7]

P. Domingos and M. Pazzani. Beyond independence: Conditions for the optimality of the simple bayesian classifier. Machine Learnin9, 29:103-130, 1997.

Digital Library

Google Scholar

[8]

Susan T. Dumais. Using LSI for information filtering: TREC-3 experiments. Technical Report 500- 225, National Institute of Standards and Technology, 1995.

Google Scholar

[9]

Jerome H. Friedman. On bias, variance, 0/1 - loss, and the curse-of-dimensionality. Data Mining and Knowledge Discovery, 1:55-77, 1997.

Digital Library

Google Scholar

[10]

Thorsten Joachims. A probabilistic analysis of the Rocchio algorithm with TFIDF for text categorization. In International Conference on Machine Learning (ICML), 1997.

Digital Library

Google Scholar

[11]

j. D. Jobson. Applied Multivariate Data Analysis - Volume iI: Categorical and Multivariate Methods. Springer Verlag, 1992.

Google Scholar

[12]

R. Kerber. Chimerge: Discretization of numeric attributes. In Proceedings of Tenth National Conference on Artificial Intelligence (AAAI-9e), 1992.

Google Scholar

[13]

D. Koller and M. Sahami. Toward optimal feature selection. In Proceedings of Thirteenth International Conference on Machine Learning (ICML-96), 1996.

Google Scholar

[14]

Ken Lang. Newsweeder: Learning to filter netnews. In International Conference on Machine Learning (ICML), pages 331-339, 1995.

Google Scholar

[15]

Lillian Lee. Similarity-Based Approaches to Natural Language Processing. PhD thesis, Harvard University, 1997. (also Technical Report TR-11-97).

Digital Library

Google Scholar

[16]

David Lewis and Marc Ringuette. A comparison of two learning algorithms for text categorization. In Third Annual Symposium on Document Analysis and Information Retrieval, pages 81-93, 1994.

Google Scholar

[17]

David D. Lewis and Kimberly A. Knowles. Threading electronic mail: A preliminary study. Information Processing and Management, 33(2):209-217, 1997.

Digital Library

Google Scholar

[18]

H. Liu and R. Setiono. Chi2: Feature selection and discretization of numeric attributes. In Proceedings of 7th IEEE Int'l Conference on Tools with Artificial Intelligence, 1995.

Digital Library

Google Scholar

[19]

Andrew McCallum and Kamal Nigam. A comparison of event models for naive Bayes text classification. In AAAI-98 Workshop on Learning for Text Categorization, 1998. http://www, cs.cmu.edu/-#mccallum.

Google Scholar

[20]

Fernando Pereira, Naftali Tishby, and Lillian Lee. Distributional clustering of english words. In Proceedings of the 31st Annual Meeting of the Association for Computational Linguistics, pages 183-90, 1993.

Digital Library

Google Scholar

[21]

WiseWire. http://www.wisewire.com.

Google Scholar

[22]

Yiming Yang. Noise reduction in a statistical approach to text categorization. In Proceedings of the 18th Annual International A CM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'95), pages 256-263, 1995.

Digital Library

Google Scholar

[23]

Yiming Yang and Jan Pederson. Feature selection in statistical learning of text categorization. In ICML- 97, pages 412-420, 1997.

Digital Library

Google Scholar

Cited By

View all

Weng ZZhang WZhu TDou ZSun HYe ZTian Y(2024)RT-APT: A Real-time APT Anomaly Detection Method for Large-scale Provenance GraphJournal of Network and Computer Applications10.1016/j.jnca.2024.104036(104036)Online publication date: Oct-2024
https://doi.org/10.1016/j.jnca.2024.104036
Amiri BKarimianghadim R(2024)A novel text clustering model based on topic modelling and social network analysisChaos, Solitons & Fractals10.1016/j.chaos.2024.114633181(114633)Online publication date: Apr-2024
https://doi.org/10.1016/j.chaos.2024.114633
Palanivinayagam AEl-Bayeh CDamaševičius R(2023)Twenty Years of Machine-Learning-Based Text Classification: A Systematic ReviewAlgorithms10.3390/a1605023616:5(236)Online publication date: 29-Apr-2023
https://doi.org/10.3390/a16050236
Show More Cited By

Index Terms

Distributional clustering of words for text classification
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by classification
    2. Machine learning approaches
      1. Classification and regression trees
2. Information systems
  1. Information retrieval
  2. Information systems applications
    1. Data mining
      1. Clustering

Recommendations

Detecting misspelled words in Turkish text using syllable n-gram frequencies
PReMI'07: Proceedings of the 2nd international conference on Pattern recognition and machine intelligence

In this study, we have designed and implemented a system which decides whether or not a word is misspelled in Turkish text. Firstly, three databases of syllable monogram, bigram and trigram frequencies are constructed using the syllables that are ...
Semantic classification of Chinese unknown words
ACL '03: Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 2

This paper describes a classifier that assigns semantic thesaurus categories to unknown Chinese words (words not already in the CiLin thesaurus and the Chinese Electronic Dictionary, but in the Sinica Corpus). The focus of the paper differs in two ways ...
Detecting Misspelled Words in Turkish Text Using Syllable n-gram Frequencies
Pattern Recognition and Machine Intelligence
Abstract
In this study, we have designed and implemented a system which decides whether or not a word is misspelled in Turkish text. Firstly, three databases of syllable monogram, bigram and trigram frequencies are constructed using the syllables that are ...

Comments

Information & Contributors

Information

Published In

SIGIR '98: Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval

August 1998

394 pages

ISBN:1581130155

DOI:10.1145/290941

Chairmen:
W. Bruce Croft
Univ. of Massachusetts
,
Alistair Moffat
Univ. of Melbourne, Victoria, Australia
,
C. J. van Rijsbergen
Univ. of Glasgow, Scotland, UK
,
Ross Wilkinson
RMIT Univ., Australia and CSIRO
,
Justin Zobel
RMIT Univ., Australia

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 August 1998

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Qualifiers

Article

Conference

SIGIR98

Sponsor:

University of Melbourne
SIGIR

SIGIR98: 21st Annual ACM/SIGIR International Conference on Research and Development in Information Retrieval

August 24 - 28, 1998

Melbourne, Australia

Acceptance Rates

Overall Acceptance Rate 792 of 3,983 submissions, 20%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

405
Total Citations
View Citations
6,822
Total Downloads

Downloads (Last 12 months)267
Downloads (Last 6 weeks)40

Reflects downloads up to 29 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

View all

Weng ZZhang WZhu TDou ZSun HYe ZTian Y(2024)RT-APT: A Real-time APT Anomaly Detection Method for Large-scale Provenance GraphJournal of Network and Computer Applications10.1016/j.jnca.2024.104036(104036)Online publication date: Oct-2024
https://doi.org/10.1016/j.jnca.2024.104036
Amiri BKarimianghadim R(2024)A novel text clustering model based on topic modelling and social network analysisChaos, Solitons & Fractals10.1016/j.chaos.2024.114633181(114633)Online publication date: Apr-2024
https://doi.org/10.1016/j.chaos.2024.114633
Palanivinayagam AEl-Bayeh CDamaševičius R(2023)Twenty Years of Machine-Learning-Based Text Classification: A Systematic ReviewAlgorithms10.3390/a1605023616:5(236)Online publication date: 29-Apr-2023
https://doi.org/10.3390/a16050236
Chai YZhang HYin QZhang J(2023)Neural Text Classification by Jointly Learning to Cluster and Align2023 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN54540.2023.10191269(1-8)Online publication date: 18-Jun-2023
https://doi.org/10.1109/IJCNN54540.2023.10191269
Dattha Tumati NShankar VHanumanthu B(2023)Effective Contact Tracing of Covid Patients Using Machine Learning Technique2023 1st International Conference on Optimization Techniques for Learning (ICOTL)10.1109/ICOTL59758.2023.10435063(1-6)Online publication date: 7-Dec-2023
https://doi.org/10.1109/ICOTL59758.2023.10435063
Zhaksybaev DBakiev M(2022)Algorithms for the classification of text documents, taking into account proximity in the attribute spaceModeling of systems and processes10.12737/2219-0767-2022-15-1-36-4315:1(36-43)Online publication date: 8-Apr-2022
https://doi.org/10.12737/2219-0767-2022-15-1-36-43
Ji SZhang ZYing SWang LZhao XGao Y(2022)Kullback–Leibler Divergence Metric LearningIEEE Transactions on Cybernetics10.1109/TCYB.2020.300824852:4(2047-2058)Online publication date: Apr-2022
https://doi.org/10.1109/TCYB.2020.3008248
Ha SGeum Y(2022)Identifying new innovative services using M&A data: An integrated approach of data-driven morphological analysisTechnological Forecasting and Social Change10.1016/j.techfore.2021.121197174(121197)Online publication date: Jan-2022
https://doi.org/10.1016/j.techfore.2021.121197
Shi YShi Y(2022)Feature SelectionAdvances in Big Data Analytics10.1007/978-981-16-3607-3_4(249-304)Online publication date: 1-Jan-2022
https://doi.org/10.1007/978-981-16-3607-3_4
Aggarwal CAggarwal C(2022)Text Classification: Basic ModelsMachine Learning for Text10.1007/978-3-030-96623-2_5(115-158)Online publication date: 10-Feb-2022
https://doi.org/10.1007/978-3-030-96623-2_5
Show More Cited By

View Options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Login options

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

Cited By

Index Terms

Recommendations

Detecting misspelled words in Turkish text using syllable n-gram frequencies

Semantic classification of Chinese unknown words

Detecting Misspelled Words in Turkish Text Using Syllable n-gram Frequencies