Abstract
This paper presents an application that belongs to automatic classification of textual data by supervised learning algorithms. The aim is to study how a better textual data representation can improve the quality of classification. Considering that a word meaning depends on its context, we propose to use features that give important information about word contexts. We present a method named GenDesc, which generalizes (with POS tags) the least relevant words for the classification task.
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
Harris, Z.: Distributional structure. Word 10, 146–162 (1954)
Joachims, T.: Text categorization with suport vector machines: Learning with many relevant features. In: Nédellec, C., Rouveirol, C. (eds.) ECML 1998. LNCS, vol. 1398, pp. 137–142. Springer, Heidelberg (1998)
Gamon, M.: Sentiment classification on customer feedback data: noisy data, large feature vectors, and the role of linguistic analysis. In: Proceedings of the 20th International Conference on Computational Linguistics, COLING 2004. Association for Computational Linguistics (2004)
Joshi, M., Penstein-Rosé, C.: Generalizing dependency features for opinion mining. In: Proceedings of the ACL-IJCNLP 2009 Conference Short Papers, ACLShort 2009, pp. 313–316. Association for Computational Linguistics (2009)
Porter, M.F.: Readings in information retrieval, 313–316. Morgan Kaufmann Publishers Inc. (1997)
Prabhakaran, V., Rambow, O., Diab, M.: Predicting overt display of power in written dialogs. In: Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 518–522 (June 2012)
Kouloumpis, E., Wilson, T., Moore, J.: Twitter sentiment analysis: The good the bad and the omg! In: Adamic, L.A., Baeza-Yates, R.A., Counts, S. (eds.) ICWSM. AAAI Press (2011)
Matsumoto, S., Takamura, H., Okumura, M.: Sentiment classification using word sub-sequences and dependency sub-trees. In: Ho, T.-B., Cheung, D., Liu, H. (eds.) PAKDD 2005. LNCS (LNAI), vol. 3518, pp. 301–311. Springer, Heidelberg (2005)
Xia, R., Zong, C.: Exploring the use of word relation features for sentiment classification. In: Proceedings of the 23rd International Conference on Computational Linguistics: Posters, COLING 2010, pp. 1336–1344. Association for Computational Linguistics (2010)
Grouin, C., Berthelin, J.B., Ayari, S.E., Heitz, T., Hurault-Plantet, M., Jardino, M., Khalis, Z., Lastes, M.: Présentation de deft 2007. In: Actes de l’atelier de clôture du 3eme Défi Fouille de Textes, pp. 1–8 (2007)
Chauché, J.: Un outil multidimensionnel de l’analyse du discours. In: Proceedings of the 10th International Conference on Computational Linguistics, COLING 1984, pp. 11–15. Association for Computational Linguistics (1984)
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The weka data mining software: an update. SIGKDD Explor. Newsl. 11, 10–18 (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Tisserant, G., Prince, V., Roche, M. (2013). GenDesc: A Partial Generalization of Linguistic Features for Text Classification. In: Métais, E., Meziane, F., Saraee, M., Sugumaran, V., Vadera, S. (eds) Natural Language Processing and Information Systems. NLDB 2013. Lecture Notes in Computer Science, vol 7934. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38824-8_35
Download citation
DOI: https://doi.org/10.1007/978-3-642-38824-8_35
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-38823-1
Online ISBN: 978-3-642-38824-8
eBook Packages: Computer ScienceComputer Science (R0)