Abstract
Document clustering has many uses in natural language tools and applications. For instance, summarizing sets of documents that all describe the same event requires first identifying and grouping those documents talking about the same event. Document clustering involves dividing a set of documents into non-overlapping clusters. In this paper, we present two document clustering algorithms: grouping algorithm, and chaining algorithm. We compared them with k-means and the EM algorithms. The evaluation results showed that our two algorithms perform better than the k-means and EM algorithms in different experiments.
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
Manning, C.D., Schutze, H.: Foundations of Statistical Natural Language Processing. MIT Press, Cambridge (2000)
Berkhin, P.: Survey of clustering data mining techniques. Technical report, Accrue Software, San Jose, CA (2002)
Duda, R., Hart, P.: Pattern Classification and Scene Analysis. John Wiley & Sons, New York (1973)
Miller, G.A., Beckwith, R., Fellbaum, C., Gross, D., Miller, K.: Five papers on wordnet. CSL Report 43, Cognitive Science Laboratory, Princeton University (1993)
Salton, G.: Automatic Text Processing: The Transformation, Analysis, and Retrieval of Information by Computer. Addison-Wesley Series in Computer Sciences, Reading (1989)
Galley, M., McKeown, K.: Improving word sense disambiguation in lexical chaining. In: Proceedings of the 18th International Joint Conference on Artificial Intelligence, Acapulco, Mexico (2003)
Barzilay, R., Elhadad, M.: Using lexical chains for text summarization. In: Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and the 8th European Chapter Meeting of the Association for Computational Linguistics, Workshop on Intelligent Scalable Text Summarization, Madrid, pp. 10–17 (1997)
Silber, H.G., McCoy, K.F.: Efficiently computed lexical chains as an intermediate representation for automatic text summarization. Computational Linguistics 28, 487–496 (2002)
Pantel, P., Lin, D.: Document clustering with committees. In: Proceedings of the ACM SIGIR 2002, Finland (2002)
Over, P. (ed.): Proceedings of the Document Understanding Conference, NIST (2003)
Over, P. (ed.): Proceedings of the Document Understanding Conference, NIST (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Chali, Y., Noureddine, S. (2005). Document Clustering with Grouping and Chaining Algorithms. In: Dale, R., Wong, KF., Su, J., Kwong, O.Y. (eds) Natural Language Processing – IJCNLP 2005. IJCNLP 2005. Lecture Notes in Computer Science(), vol 3651. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11562214_25
Download citation
DOI: https://doi.org/10.1007/11562214_25
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29172-5
Online ISBN: 978-3-540-31724-1
eBook Packages: Computer ScienceComputer Science (R0)