Abstract
In the general framework of knowledge discovery, Data Mining techniques are usually dedicated to information extraction from structured databases. Text Mining techniques, on the other hand, are dedicated to information extraction from unstructured textual data and Natural Language Processing (NLP) can then be seen as an interesting tool for the enhancement of information extraction procedures. In this paper, we present two examples of Text Mining tasks, association extraction and prototypical document extraction, along with several related NLP techniques.
Chapter PDF
Similar content being viewed by others
References
Brill E. (1992) A Simple Rule-Based Part-of-Speech Tagger. In Proc. of the 3rd Conf. on Applied Natural Language Processing.
Cutting D. (1992) A Practical Part-of-Speech Tagger. In Proc. of the 3rd Conf. on Applied Natural Language Processing.
Daille B. (1994) Study and Implementation of Combined Techniques for Automatic Extraction of Terminology. In Proc. of the 32nd Annual Meeting of the Association for Computational Linguistics.
Džerovski S. (1996) Inductive logic programming and Knowledge Discovery in Databases. In Advances in Knowledge Discovery and Data Mining. AAAI Press/The MIT Press.
Fayyad U.M., Piatetsky-Shapiro G. and Smyth P. (1996) From Data Mining to Knowledge Discovery: An Overview. In Advances in Knowledge Discovery and Data Mining. AAAI Press/The MIT Press.
Feldman R., Dagan I. and Kloegsen W. (1996) Efficient Algorithm for Mining and Manipulating Associations in Texts. 13 th European Meeting on Cybernetics and Research.
Feldman R. and Hirsh H. (1996) Mining Associations in Text in the Presence of Background Knowledge. In Proc. of the 2nd Mt. Conf. on Knowledge Discovery.
Feldman R. and Hirsh H. (1997) Finding Associations in Collections of Text. In Michalski R.S., Bratko I. and Kubat M. (edts) Machine Learning, Data Mining and Knowledge Discovery: Methods and Application (John Wiley and sons Ltd).
MITRE NLP Group (1997) Alembic Language Processing for Intelligence Applications. At URL: http://www.mitre.org/resources/centers/advancedinfo/g04h/nl-index.html
Rajman M. and Besançon R. (1997) A Lattice Based Algorithm for Text Mining. Technical Report TR-LIA-LN1/97, Swiss Federal Institute of Technology.
Salton G. and Buckley C. (1988) Term Weighting Approaches in Automatic Text Retrieval. Information Processing and Management, 24: 5, 513–523.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1998 Springer Science+Business Media Dordrecht
About this chapter
Cite this chapter
Rajman, M., Besançon, R. (1998). Text Mining: Natural Language techniques and Text Mining applications. In: Spaccapietra, S., Maryanski, F. (eds) Data Mining and Reverse Engineering. IFIP — The International Federation for Information Processing. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-35300-5_3
Download citation
DOI: https://doi.org/10.1007/978-0-387-35300-5_3
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4757-4910-6
Online ISBN: 978-0-387-35300-5
eBook Packages: Springer Book Archive