Abstract
Hierarchical text classification plays an important role in many real-world applications, such as webpage topic classification, product categorization and user feedback classification. Usually a large number of training examples are needed to build an accurate hierarchical classification system. Active learning has been shown to reduce the training examples significantly, but it has not been applied to hierarchical text classification due to several technical challenges. In this paper, we study active learning for hierarchical text classification. We propose a realistic multi-oracle setting as well as a novel active learning framework, and devise several novel leveraging strategies under this new framework. Hierarchical relation between different categories has been explored and leveraged to improve active learning further. Experiments show that our methods are quite effective in reducing the number of oracle queries (by 74% to 90%) in building accurate hierarchical classification systems. As far as we know, this is the first work that studies active learning in hierarchical text classification with promising results.
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References
Ceci, M., Malerba, D.: Classifying web documents in a hierarchy of categories: a comprehensive study. J. Intell. Inf. Syst. 28, 37–78 (2007)
D’Alessio, S., Murray, K., Schiaffino, R., Kershenbaum, A.: The effect of using hierarchical classifiers in text categorization. In: RIAO 2000, pp. 302–313 (2000)
Daraselia, N., Yuryev, A., Egorov, S., Mazo, I., Ispolatov, I.: Automatic extraction of gene ontology annotation and its correlation with clusters in protein networks. BMC Bioinformatics 8(1), 243 (2007)
Donmez, P., Carbonell, J.G.: Proactive learning: cost-sensitive active learning with multiple imperfect oracles. In: CIKM 2008, pp. 619–628 (2008)
Esuli, A., Sebastiani, F.: Active Learning Strategies for Multi-Label Text Classification. In: Boughanem, M., Berrut, C., Mothe, J., Soule-Dupuy, C. (eds.) ECIR 2009. LNCS, vol. 5478, pp. 102–113. Springer, Heidelberg (2009)
Fagni, T., Sebastiani, F.: Selecting negative examples for hierarchical text classification: An experimental comparison. J. Am. Soc. Inf. Sci. Technol. 61, 2256–2265 (2010)
Fan, R.E., Chang, K.W., Hsieh, C.J., Wang, X.R., Lin, C.J.: Liblinear: A library for large linear classification. The Journal of Machine Learning Research 9, 1871–1874 (2008)
Lam, W., Ho, C.Y.: Using a generalized instance set for automatic text categorization. In: SIGIR 1998, pp. 81–89 (1998)
Lewis, D.D., Yang, Y., Rose, T.G., Li, F.: Rcv1: A new benchmark collection for text categorization research. J. Mach. Learn. Res. 5, 361–397 (2004)
Nowak, S., Rüger, S.: How reliable are annotations via crowdsourcing: a study about inter-annotator agreement for multi-label image annotation. In: MIR 2010, pp. 557–566 (2010)
Platt, J.C.: Probabilistic outputs for support vector machines. In: Advances in Large Margin Classifiers, pp. 61–74 (1999)
Roy, N., McCallum, A.: Toward optimal active learning through sampling estimation of error reduction. In: ICML 2001, pp. 441–448 (2001)
Ruiz, M.E., Srinivasan, P.: Hierarchical neural networks for text categorization (poster abstract). In: SIGIR 1999, pp. 281–282 (1999)
Silla Jr., C.N., Freitas, A.A.: A survey of hierarchical classification across different application domains. Data Min. Knowl. Discov. 22, 31–72 (2011)
Sun, A., Lim, E.-P.: Hierarchical text classification and evaluation. In: ICDM 2001, pp. 521–528 (2001)
Tong, S., Koller, D.: Support vector machine active learning with applications to text classification. J. Mach. Learn. Res. 2, 45–66 (2002)
Verspoor, K., Cohn, J., Mniszewski, S., Joslyn, C.: Categorization approach to automated ontological function annotation. In: Protein Science, pp. 1544–1549 (2006)
Xu, Z., Yu, K., Tresp, V., Xu, X., Wang, J.: Representative Sampling for Text Classification Using Support Vector Machines. In: Sebastiani, F. (ed.) ECIR 2003. LNCS, vol. 2633, pp. 393–407. Springer, Heidelberg (2003)
Xue, G.R., Xing, D., Yang, Q., Yu, Y.: Deep classification in large-scale text hierarchies. In: SIGIR 2008, pp. 619–626 (2008)
Yang, B., Sun, J.T., Wang, T., Chen, Z.: Effective multi-label active learning for text classification. In: KDD 2009, pp. 917–926 (2009)
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Li, X., Kuang, D., Ling, C.X. (2012). Active Learning for Hierarchical Text Classification. In: Tan, PN., Chawla, S., Ho, C.K., Bailey, J. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2012. Lecture Notes in Computer Science(), vol 7301. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30217-6_2
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DOI: https://doi.org/10.1007/978-3-642-30217-6_2
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