Abstract
In this paper we present a method for the automatic discovery and tuning of term similarities. The method is based on the automatic extraction of significant patterns in which terms tend to appear. Beside that, we use lexical and functional similarities between terms to define a hybrid similarity measure as a linear combination of the three similarities. We then present a genetic algorithm approach to supervised learning of parameters that are used in this linear combination. We used a domain specific ontology to evaluate the generated similarity measures and set the direction of their convergence. The approach has been tested and evaluated in the domain of molecular biology.
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
Frantzi, K., Ananiadou, S., Mima, H.: Automatic Recognition of Multi-Word Terms. Int. J. on Digital Libraries 3/2 (2000) 117–132
Hearst, M.: Automatic Acquisition of Hyponyms From Large Text Corpora. Proceedings of Coling 92, Nantes, France (1992)
Maynard, D., Ananiadou, S.: Identifying Terms by Their Family and Friends. Proceedings of Coling 2000, Luxembourg (2000) 530–536
Medline: National Library of Medicine. http://www.ncbi.nlm.nih.gov/PubMed/ (2002)
Mima, H., Ananiadou, S., Nenadić, G.: Atract Workbench: An Automatic Term Recognition and Clustering of Terms. In: Matoušek, V. et al. (eds.): Text, Speech and Dialogue — Tsd 2001. LNAI 2166. Springer Verlag (2001) 126–133
Reeves, C.: Modern Heuristic Techniques. In: Rayward-Smith, V. et. al (eds.) Modern Heuristic Search Methods. John Wiley & Sons Ltd., New York (1996) 1–25
Santini, S., Jain, R.: Similarity Measures. IEEE Transactions on Pattern Analysis and Machine Intelligence 21/9 (1999) 871–883
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2002 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Spasić, I., Nenadić, G., Manios, K., Ananiadou, S. (2002). Supervised Learning of Term Similarities. In: Yin, H., Allinson, N., Freeman, R., Keane, J., Hubbard, S. (eds) Intelligent Data Engineering and Automated Learning — IDEAL 2002. IDEAL 2002. Lecture Notes in Computer Science, vol 2412. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45675-9_64
Download citation
DOI: https://doi.org/10.1007/3-540-45675-9_64
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-44025-3
Online ISBN: 978-3-540-45675-9
eBook Packages: Springer Book Archive