Abstract
Context affects many aspects of the behavior. Natural language understanding is one of the prime examples. This paper summarizes how an artificial neural network, the self-organizing map, can be used in modeling contextuality in data analysis and natural language processing. Important aspects are adaptivity gained by using a learning system, autonomous nature of the processing based on unsupervised learning paradigm, and gradedness of the representation. Examples in the application areas of information retrieval and knowledge management are considered. For instance, the visualization of self-organizing maps provides meaningful context for documents.
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
Honkela, T., Pulkki, V. and Kohonen, T. (1995). Contextual relations of words in Grimm tales analyzed by self-organizing map. Proceedings of ICANN-95, International Conference on Artificial Neural Networks, F. Fogelman-Soulié and P. Gallinari (eds), vol. 2, EC2 et Cie, Paris, pp. 3–7.
Honkela, T., Kaski, S., Lagus, K., and Kohonen, T. (1996). Newsgroup exploration with WEBSOM method and browsing interface. Technical Report A32, Helsinki University of Technology, Lab. of Computer and Information Science, Espoo, Finland.
Honkela, T. (1998). Kohonen’s Self-Organizing Maps in Intelligent Systems Development. Proceedings of FODO’98, The 5th International Conference on Foundations of Data Organization, Kobe, Japan, pp. 13–19.
Hörmann, H. (1986). Meaning and Context. Plenum Press, New York.
Kaski, S., Honkela, T., Lagus, K., and Kohonen, T. (1996). Creating an order in digital libraries with self-organizing maps. In Proceedings of WCNN-96, World Congress on Neural Networks.
Kaski, S. (1997b). Data exploration using self-organizing maps. Acta Polytechnica Scandinavica, Mathematics, Computing and Management in Engineering Series No. 82. DTech Thesis, Helsinki University of Technology, Finland.
Kaski, S., Honkela, T., Lagus, K., and Kohonen, T. (1998). WEBSOM-self-organizing maps of document collections, Neurocomputing, vol. 21, pp. 101–117.
Kaski, S., Kangas, J., and Kohonen, T. (1998). Bibliography of Self-Organizing Map (SOM) Papers: 1981—1997. Neural Computing Surveys, 1: 102–350. Available also at http://www.icsi.berkeley.edu/~jagota/NCS/
Kohonen, T. (1982). Self-organizing formation of topologically correct feature maps. Biological Cybernetics, 43(1):59–69.
Kohonen, T. (1995). Self-Organizing Maps. Springer, Berlin, Heidelberg.
Kohonen, T., Kaski, S., Lagus, K., and Honkela, T. (1996b). Very large two-level SOM for the browsing of newsgroups. In von der Malsburg, von Seelen, Vorbrüggen, and Sendhoff, editors, Proceedings of ICANN96, International Conference on Artificial Neural Networks, Bochum, Germany, July 16-19, 1996, Lecture Notes in Computer Science, vol. 1112, pp. 269–274. Springer, Berlin.
Lagus, K., Honkela, T., Kaski, S., and Kohonen, T. (1996). Self-organizing maps of document collections: A new approach to interactive exploration. In Simoudis, E., Han, J., and Fayyad, U., editors, Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, pp. 238–243. AAAI Press, Menlo Park, California.
Lin, X., Soergel, D., and Marchionini, G. (1991). A self-organizing semantic map for information retrieval. In Proceedings of 14th. Ann. International ACM/SIGIR Conference on Research & Development in Information Retrieval, pp. 262–269.
Pulkki, V. (1995). Data averaging inside categories with the self-organizing map. Report A27, Helsinki University of Technology, Laboratory of Computer and Information Science, Espoo, Finland.
Ritter, H. and Kohonen, T. (1989). Self-organizing semantic maps. Biological Cybernetics, 61(4):241–254.
Scholtes, J. C. (1992b). Resolving linguistic ambiguities with a neural data-oriented parsing (DOP) system. In Aleksander, I. and Taylor, J., editors, Artificial Neural Networks, 2, volume II, pp. 1347–1350, North-Holland, Amsterdam, Netherlands.
Schütze, H. (1992). Dimensions of meaning. In Proceedings of Supercomputing, pp. 787–796.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1999 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Honkela, T. (1999). Connectionist Analysis and Creation of Context for Natural Language Understanding and Knowledge Management. In: Bouquet, P., Benerecetti, M., Serafini, L., Brézillon, P., Castellani, F. (eds) Modeling and Using Context. CONTEXT 1999. Lecture Notes in Computer Science(), vol 1688. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48315-2_43
Download citation
DOI: https://doi.org/10.1007/3-540-48315-2_43
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-66432-1
Online ISBN: 978-3-540-48315-1
eBook Packages: Springer Book Archive