Abstract
The flood of electronic information is pouring over us, while the technology maintaining the information and making it available to us has not yet been able to catch up. One of the paradigms within information retrieval focuses on the use of thesauruses to analyze contextual/structural information. We have explored a method that automatically finds textual relations in electronic documents using genetic programming and semantic networks. Such textual relations can be used to extend and update thesauruses as well as semantic networks. The program is written in PROLOG and communicates with software for natural language parsing. The system is also an example of computationally expensive fitness function using a large database. The results from the experiment show feasibility for this type of automatic relation extraction.
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 subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Banzhaf, W., Nordin, P., Keller, R.E., Francone, F.D.: Genetic Programming: An Introduction on the Automatic Evolution of Computer Programs and Its Applications. Morgan Kaufmann, Germany (1997)
Fellbaum, C.: WordNet - An Electronic Lexical Database. MIT Press, Cambridge (1998)
Freitas, A.A.: A genetic programming framework for two data mining tasks: Classification and generalized rule induction. In: Genetic Programming 1997: Proceedings of the Second Annual Conference, Stanford University, CA, USA, pp. 96–101 (1997)
Gauch, S., Smith, J.B.: An Expert System for Searching Full Text. Information Processing and Management 25(3) (1989)
Gazdar, G., Mellish, C.: Natural Language Processing in PROLOG: An Introduction to Computational Linguistics. Addison-Wesley Publishing Company, Wokingham (1994)
Hearst, M.: Automatic Acquistion of Hyponyms from Large Text Corpora. In: Proceedings of the Fourteenth International Conference on Computational Linguistics, Nantes, France (1992)
Nelson, M.R.: We Have the Information You Want, But Getting It Will Cost You: Being Held Hostage by Information Overload. Crossroads 4(1) (Fall 1997)
Marcus, R.S.: Computer and Human Understanding in Intelligent Retrieval Assistance. In: Proceedings of the 54th American Society for Information Science meeting, vol. 28 (1991)
Masand, B.: Optimizing Confidence of Text classification by Evolution of Symbolic Expressions. In: Advances in Genetic Programming. MIT Press, USA (1994)
Salton, G.O., McGill, M.J.: Introduction to Modern Information Retrieval. McGraw-Hill, New York (1983)
Salton, G.: Automatic Text Processing: The Transformation, Analysis, and Retrieval of Information by Computer. Addison-Wesley Publishing, Reading (1989)
Smith, T.C., Witten, I.H.: A genetic algorithm for the induction of natural language grammars. In: Proceeding of IJCAI 1995 Workshop on New Approaches to Learning for Natural Language Processing, Montreal, Canada, pp. 17–24 (1995)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2000 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Bergström, A., Jaksetic, P., Nordin, P. (2000). Acquiring Textual Relations Automatically on the Web Using Genetic Programming. In: Poli, R., Banzhaf, W., Langdon, W.B., Miller, J., Nordin, P., Fogarty, T.C. (eds) Genetic Programming. EuroGP 2000. Lecture Notes in Computer Science, vol 1802. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-46239-2_17
Download citation
DOI: https://doi.org/10.1007/978-3-540-46239-2_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-67339-2
Online ISBN: 978-3-540-46239-2
eBook Packages: Springer Book Archive