Abstract
In this paper, the effect of different windowing schemes to the success rate of word sense disambiguation is probed. In these windowing schemes it is considered that the impact of a neighbor word to the correct sense of the target word should be somewhat related to it’s distance to the target word. Several weighting functions are evaluated for their performance in representing this relation. Two semantic similarity measures, one of which is introduced by the authors of this paper, are used in a modified version of Maximum Relatedness Disambiguation algorithm for the experiments. This approach yielded improvements up to 4.24% in word sense disambiguation accuracy.
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
Fellbaum, C. (ed.): WordNet: An electronic lexical database. Language, Speech, and Communication. MIT Press, Cambridge (1998)
Banerjee, S., Pedersen, T.: An adapted Lesk algorithm for word sense disambiguation using WordNet. In: Proceedings of Intelligent Text Processing and Computational Linguistics, Mexico City (2002)
Quillian, M.R.: Semantic memory. In: Minsky, M. (ed.) Semantic Information Processing, pp. 227–270. MIT Press, Cambridge (1968)
Wu, Z., Palmer, M.: Verb Semantics and Lexical Selection. In: Proceedings of the 32nd Annual Meeting of the Association for Computational Linguistics, Las Cruces, New Mexico (1994)
Resnik, P.: Using information content to evaluate semantic similarity. In: Proceedings of the 14th International Joint Conference on Artificial Intelligence, Montreal, pp. 448–453 (1995)
Jiang, J., Conrath, D.: Semantic similarity based on corpus statistics and lexical taxonomy. In: Proceedings of International Conference on Research in Computational Linguistics, Taiwan, pp. 19–33 (1997)
Lin, D.: An information-theoretic definition of similarity. In: Proceedings of the 15th International Conference on Machine Learning, Madison, Wisconsin (1998)
Hirst, G., St-Onge, D.: Lexical Chains as representations of context for the detection and correction of malapropisms. In: Fellbaum, C. (ed.) WordNet: An electronic lexical database. Language, Speech, and Communication, pp. 305–332. MIT Press, Cambridge (1998)
Leacock, C., Chodorow, M.: Combining local context and WordNet similarity for word sense identification. In: Fellbaum, C. (ed.) WordNet: An electronic lexical database. Language, Speech, and Communication, pp. 265–283. MIT Press, Cambridge (1998)
Rennie, J.: WordNet::QueryData: a Perl module for accessing the WordNet database (2000), http://search.cpan.org/dist/WordNet-QueryData
Pedersen, T., Patwardhan, S., Michelizzi, J.: WordNet::Similarity - Measuring the Relatedness of Concepts, pp. 1024–1025. AAAI, Menlo Park (2004)
Patwardhan, S., Banerjee, S., Pedersen, T.: Using Semantic Relatedness for Word Sense Disambiguation. In: Proceedings of the Fourth International Conference on Intelligent Text Processing and Computational Linguistics, Mexico City, pp. 241–257 (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Altintas, E., Karsligil, E., Coskun, V. (2005). The Effect of Windowing in Word Sense Disambiguation. In: Yolum, p., Güngör, T., Gürgen, F., Özturan, C. (eds) Computer and Information Sciences - ISCIS 2005. ISCIS 2005. Lecture Notes in Computer Science, vol 3733. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11569596_65
Download citation
DOI: https://doi.org/10.1007/11569596_65
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29414-6
Online ISBN: 978-3-540-32085-2
eBook Packages: Computer ScienceComputer Science (R0)