Abstract
This paper proposes a Fully Automatic Categorization approach for Text (FACT) by exploiting the semantic features from WordNet and document clustering. In FACT, the training data is constructed automatically by using the knowledge of the category name. With the support of WordNet, it first uses the category name to generate a set of features for the corresponding category. Then, a set of documents is labeled according to such features. To reduce the possible bias originating from the category name and generated features, document clustering is used to refine the quality of initial labeling. The training data are subsequently constructed to train the discriminative classifier. The empirical experiments show that the best performance of FACT can achieve more than 90% of the baseline SVM classifiers in F1 measure, which demonstrates the effectiveness of the proposed approach.
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References
Gliozzo, A.M., Strapparava, C., Dagan, I.: Investigating Unsupervised Learning for Text Categorization Bootstrapping. In: Proc. of EMNLP (2005)
Liu, B., Li, X., Lee, W.S., Yu, P.S.: Text Classification by Labeling Words. In: Proc. 19th Nat’l Conf. Artificial Intelligence (2004)
Blum, A., Mitchell, T.: Combining labeled and unlabeled data with co-training. In: Proc. of the Workshop on Computational Learning Theory (1998)
de Buenaga Rodriguez, M., Gomez-Hidalgo, J., Diaz- Agudo, B.: Using WordNet to complement training information in text categorization. In: Proc. of RANLP (1997)
Hotho, A., Staab, S., Stumme, G.: Wordnet Improves Text Document Clustering. In: Proc. of the Semantic Web Workshop at SIGIR (2003)
Witten, I.H., Frank, E.: Data Mining: Practical machine learning tools and techniques, 2nd edn. Morgan Kaufmann, San Francisco (2005)
Ide, N., Véronis, J.: Word sense disambiguation: The state of the art. Computational Linguistics 24(1), 1–40 (1998)
Joachims, T.: Transductive inference for text classification using support vector machines. In: Proc. 16th International Conf. on Machine Learning, pp. 200–209 (1999)
Kehagias, A., Petridis, V., Kaburlasos, V., Fragkou, P.: A comparison of word- and sense-based text classification using several classification algorithms. Journal of Intelligent Information Systems 21(3), 227–247 (2003)
Moldovan, D.I., Mihalcea, R.: Using WordNet and Lexical Operators to Improve Internet Searches. IEEE lnternet Computing 4(1), 34–43 (2000)
Nigam, K., McCallum, A., Thrun, S., Mitchell, T.: Text classification from labeled and unlabeled documents using EM. Machine Learning, 103–134 (2000)
Scott, S., Matwin, S.: Text classification using WordNet hypernyms. In: Proc. Coling-ACL 1998, pp. 45–52 (1998)
Peng, X., Choi, B.: Document classifications based on word semantic hierarchies. In: Proc. of the International Conf. on Artificial Intelligence and Application (AIA 2005), pp. 362–367 (2005)
Banerjee, S., Pedersen, T.: An Adapted Lesk Algorithm for Word Sense Disambiguation Using WordNet. In: Gelbukh, A. (ed.) CICLing 2002. LNCS, vol. 2276, pp. 136–145. Springer, Heidelberg (2002)
Sebastiani, F.: Machine learning in automated text categorization. ACM Computing Surveys 34(1), 1–47 (2002)
Mansuy, T.N., Hilderman, R.J.: A Characterization of Wordnet Features in Boolean Models For Text Classification. In: AusDM 2006, pp. 103–109 (2006)
Vapnik, V.: The nature of statistical learning theory. Springer, Heidelberg (1995)
Chen, W., Zhu, J., Wu, H., Yao, T.: Automatic learning features using bootstrapping for text categorization. In: Gelbukh, A. (ed.) CICLing 2004. LNCS, vol. 2945, pp. 571–579. Springer, Heidelberg (2004)
Zhu, X.-J.: Semi-Supervised Learning Literature Survey (2007), http://pages.cs.wisc.edu/~jerryzhu/research/ssl/semireview.html
Ko, Y., Seo, J.: Automatic text categorization by unsupervised learning. In: Proc. of COLING 2000 (2000)
Yang, Y., Liu, X.: A re-examination of text categorization methods. In: Proc. of SIGIR 1999 (1999)
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Li, J., Zhao, Y., Liu, B. (2009). Fully Automatic Text Categorization by Exploiting WordNet. In: Lee, G.G., et al. Information Retrieval Technology. AIRS 2009. Lecture Notes in Computer Science, vol 5839. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04769-5_1
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DOI: https://doi.org/10.1007/978-3-642-04769-5_1
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