Abstract
In this article we compare the performance of various machine learning algorithms on the task of constructing word-sense disambiguation rules from data. The distinguishing characteristic of our work from most of the related work in the field is that we aim at the disambiguation of all content words in the text, rather than focussing on a small number of words. In an earlier study we have shown that a decision tree induction algorithm performs well on this task. This study compares decision tree induction with other popular learning methods and discusses their advantages and disadvantages. Our results confirm the good performance of decision tree induction, which outperforms the other algorithms, due to its ability to order the features used for disambiguation, according to their contribution in assigning the correct sense.
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
Black E.: An experiment in computational discrimination of English word senses. IBM Journal of Research and Development, v. 32, n. 2, (1988) 185–194
Bruce, R. and Guthrie, L.: Genus Disambiguation: A study in weighted preference. In Proceedings of the International Conference on Computational Linguistics, (1992) 1187–1191
Clark, P. and Niblett, T.: The CN2 algorithm. Machine Learning, 3(4), (1989) 261–283.
Cowie, J., Guthrie, J. A. and Guthrie, L.: Lexical disambiguation using simulated annealing. In Proceedings of the International Conference on Computational Linguistics, (1992) 359–365
Duda, R. O. and Hart, P. E.: Pattern Classification and Scene Analysis, John Wiley, (1973)
Gale, W. A., Church, K. W. and Yarowsky, D.: A method for disambiguating word senses in a large corpus. Computers and the Humanities, v. 26, (1993) 415–439
Ide, N. and Veronís, J.: Introduction to the special issue on Word Sense Disambiguation: The state of the art. Computational Linguistics, v. 24, n. 1, (1998) 1–40
Kohavi R: The Power of Decision Tables. In Proceedings of the European Conference on Machine Learning, (1995) 174–189
Leacock, C., Towell, G. and Voorhees, E. M.: Corpus-based statistical sense resolution. In Proceedings of the ARPA Human Languages Technology Workshop (1993)
Leacock, C., Chodrow, M. and Miller, G. A.: Using corpus statistics and WordNet relations for sense identification. Computational Linguistics, v. 24, n. 1, (1998) 147–165
Lesk, M.: Automated sense disambiguation using machine-readable dictionaries: How to tell an pine cone from an ice cream cone. In Proceedings of the SIGDOC Conference, (1986) 24–26
Michalski, R. S., Mozetic, I., Hong, J. and Lavrac, N.: The multi-purpose incremental learning system AQ15 and its testing application to three medical domains. In Proceedings of the National Conference on Artificial Intelligence, (1986) 1041–1045.
Mitchell, T. M., Machine Learning, McGraw-Hill (1997)
Paliouras, G., Karkaletsis, V. and Spyropoulos, C. D.,: Machine Learning for Domain-Adaptive Word Sense Disambiguation. In Proceedings of the Workshop on Adapting Lexical and Corpus Resources to Sublanguages and Applications, International Conference on Language Resources and Evaluation, Granada, Spain, May 26 (1998)
Paliouras, G., Karkaletsis V. and Spyropoulos, C. D.: Learning Rules for Large Vocabulary Word Sense Disambiguation. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI’ 99), v. 2, 674–679 (1999)
Quinlan, J. R.: C4.5: Programs for machine learning, Morgan-Kaufmann (1993)
Sch:utze, H.: Automatic word sense discrimination. Computational Linguistics, v. 24, n. 1, (1998) 97–124
Towell, G. and Voorhees, E. M.: Disambiguating highly ambiguous words. Computational Linguistics, v. 24, n. 1, (1998) 125–146
Wilks, Y. A., Fass, D. C, Guo, M., MacDonald, J. E., Plate, T. and Slator, B.M: Providing machine tractable dictionary tools. Machine Translation, v. 5, (1990) 99–154
Witten, I.H. and Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations, Morgan-Kaufmann (1999)
Yarowsky, D.: Word-sense disambiguation using statistical models of Rogetrss categories trained on large corpora. In Proceedings of the International Conference in Computational Linguistics, (1992) 454–460
Yarowsky, D.: Decision lists for lexical ambiguity resolution: Application to accent restoration in Spanish and French. In Proceedings of the Annual Meeting of the Association for Computational Linguistics, (1994) 88–95
Yarowsky, D.: Unsupervised word sense disambiguation rivaling supervised methods. In Proceedings of the Annual Meeting of the Association for Computational Linguistics, (1995) 189–196
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
Paliouras, G., Karkaletsis, V., Androutsopoulos, I., Spyropoulos, C.D. (2000). Learning Rules for Large-Vocabulary Word Sense Disambiguation: A Comparison of Various Classifiers. In: Christodoulakis, D.N. (eds) Natural Language Processing — NLP 2000. NLP 2000. Lecture Notes in Computer Science(), vol 1835. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45154-4_35
Download citation
DOI: https://doi.org/10.1007/3-540-45154-4_35
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-67605-8
Online ISBN: 978-3-540-45154-9
eBook Packages: Springer Book Archive