Abstract
In this chapter case studies for text-mining applications are presented. Each case study is examined for the following characteristics: (a) problem description, (b) solution overview, (c) methods and procedures, and (d) system deployment. The following applications are reviewed: market intelligence from the web, lightweight document matching for digital libraries, generating model cases for help desk applications, assigning topics to news articles, e-mail filtering, search engines, extracting named entities from documents, and customized newspapers.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
F. Damerau, T. Zhang, S. Weiss, and N. Indurkhya. Text categorization for a comprehensive time-dependent benchmark. Information Processing and Management, 40(2):209–221, 2004.
P. Graham. Better Bayesian filtering. In Proceedings of the 2003 Spam Conference, 2003. http://spamconference.org/proceedings2003.html.
K. McKeown, R. Barzilay, D. Evans, V. Hatzivassiloglou, J. Klavans, A. Nenkova, C. Sable, B. Schiffman, and S. Sigelman. Tracking and summarizing news on a daily basis with Columbia’s newsblaster. In Proceedings of the Human Languages Technology Conference. ACL, East Stroudsburg, 2002.
E. Sang and F. De Meulder. Introduction to the CoNLL-2003 shared task: Language independent named entity recognition. In W. Daelemans and M. Osborne, editors, Proceedings of CoNLL-2003, pages 142–147. ACL, East Stroudsburg, 2003.
S. Weiss and N. Verma. A system for real-time competitive market intelligence. In Proceedings of SIGKDD-2002. ACM, New York, 2002.
S. Weiss, B. White, and C. Apté. Lightweight document clustering. In Proceedings of PKDD-2000, pages 665–672. Springer, New York, 2000a.
S. Weiss, B. White, C. Apté, and F. Damerau. Lightweight document matching for help-desk applications. IEEE Intelligent Systems, 15(2):57–61, 2000b.
T. Zhang and D. Johnson. A robust risk minimization based named entity recognition system. In Proceedings of CoNLL-2003, pages 204–207. ACL, East Stroudsburg, 2003.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2010 Springer-Verlag London Limited
About this chapter
Cite this chapter
Weiss, S.M., Indurkhya, N., Zhang, T. (2010). Case Studies. In: Fundamentals of Predictive Text Mining. Texts in Computer Science. Springer, London. https://doi.org/10.1007/978-1-84996-226-1_8
Download citation
DOI: https://doi.org/10.1007/978-1-84996-226-1_8
Publisher Name: Springer, London
Print ISBN: 978-1-84996-225-4
Online ISBN: 978-1-84996-226-1
eBook Packages: Computer ScienceComputer Science (R0)