Abstract
As any other classification task, Word Sense Disambiguation requires a large number of training examples. These examples, which are easily obtained for most of the tasks, are particularly difficult to obtain for this case. Based on this fact, in this paper we investigate the possibility of using a Web-based approach for determining the correct sense of an ambiguous word based only in its surrounding context. In particular, we propose a semi-supervised method that is specially suited to work with just a few training examples. The method considers the automatic extraction of unlabeled examples from the Web and their iterative integration into the training data set. The experimental results, obtained over a subset of ten nouns from the SemEval lexical sample task, are encouraging. They showed that it is possible to improve the baseline accuracy of classifiers such as Naïve Bayes and SVM using some unlabeled examples extracted from the Web.
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
Aguirre, E., Rigau, G.: A Proposal for Word Sense Disambiguation using Conceptual Distance. In: Proc. of the Int. Conf. on Recent Advances in NLP. RANLP 1995 (1995)
Blum, A., Mitchell, T.: Combining labeled and unlabeled data with co-training. In: Proc. COLT, pp. 92–100 (1998)
Buscaldi, D., Rosso, P.: A conceptual density-based approach for the disambiguation of toponyms. International Journal of Geographical Information Science 22(3), 143–153 (2008)
Goldman, S., Zhou, Y.: Enhancing supervised learning with unlabeled data. In: Proc. ICML, pp. 327–334 (2000)
Guzmán-Cabrera, R., Montes-y-Gómez, M., Rosso, P., Villaseñor-Pineda, L.: Using the Web as Corpus for Self-training Text Categorization. Journal of Information Retrieval (forthcoming, 2009) ISSN 1386-4564
Ide, N., Veronis, J.: Introduction to the special Issue on word sense disambiguation: the state of the art, Computational Linguistics. Special Issue on word sense Disambiguation 24(1), 1–40 (1998)
Kilgarriff, A., Greffenstette, G.: Introduction to the Special Issue on Web as Corpus. Computational Linguistics 29(3), 1–15 (2003)
Lee, Y.K., Ng, H.T.: An empirical evaluation of knowledge sources and learning algorithms for word sense disambiguation. In: Proc. EMNLP, pp. 41–48 (2002)
Mihalcea, R.: Co-training and Self-training for Word Sense Disambiguation. In: Proc. CoNLL, pp. 33–40 (2004)
Pham, T.P., Ng, H.T., Lee, W.S.: Word Sense Disambiguation with Semi-Supervised Learning. In: Proc. AAAI, pp. 1093–1098 (2005)
Pinto, D.: On Clustering and Evaluation of Narrow Domain Short-Text Corpora. PhD thesis, Universidad Politécnica de Valencia, Spain (2008)
Solorio, T.: Using unlabeled data to improve classifier accuracy. M.Sc. thesis, Computer Science Department, INAOE, Mexico (2002)
Su, W., Carpuat, M., Wu, D.: Semi-Supervised Training of a Kernel PCA-Based Model for Word Sense Disambiguation. In: Proc. COLING, pp. 1298–1304 (2004)
Tratz, S., Sanfilippo, A., Gregory, M., Chappell, A., Posse, C., Paul, W.: PNNL: A Supervised Maximum Entropy Approach to Word Sense Disambiguation. In: Proceedings of the 4th International Workshop on Semantic Evaluations (SemEval. 2007), pp. 264–267 (2007)
Yarowsky, D.: Unsupervised Word Sense Disambiguation Rivaling Supervised Methods. In: Proc. ACL, pp. 189–196 (1995)
Yu, N.Z., Hong, J.D., Lim, T.C.: Word Sense Disambiguation Using Label Propagation Based Semi-supervised Learning Method. In: Proc. ACL, pp. 395–402 (2005)
Zelikovitz, S., Kogan, M.: Using Web Searches on Important Words to Create Background Sets for LSI Classification. In: 19th Int. FLAIRS Conf., Melbourne Beach, Florida (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Guzmán-Cabrera, R., Rosso, P., Montes-y-Gómez, M., Villaseñor-Pineda, L., Pinto-Avendaño, D. (2009). Semi-supervised Word Sense Disambiguation Using the Web as Corpus. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2009. Lecture Notes in Computer Science, vol 5449. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00382-0_21
Download citation
DOI: https://doi.org/10.1007/978-3-642-00382-0_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-00381-3
Online ISBN: 978-3-642-00382-0
eBook Packages: Computer ScienceComputer Science (R0)