Abstract
Traditional approach for building text classifiers usually require a lot of labeled documents, which are expensive to obtain. In this paper, we propose a new text classification approach based on a keyword and Wikipedia knowledge, so as to avoid labeling documents manually. Firstly, we retrieve a set of related documents about the keyword from Wikipedia. And then, with the help of related Wikipedia pages, more positive documents are extracted from the unlabeled documents. Finally, we train a text classifier with these positive documents and unlabeled documents. The experiment result on 20Newsgroup dataset show that the proposed approach performs very competitively compared with NB-SVM, a PU learner, and NB, a supervised learner.
This work is supported by Young Cadreman Supporting Program of Northwest A&F University (01140301).
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
Blum, A., Mitchell, T.: Combining labeled and unlabeled data with co-training. In: Proceedings of the eleventh annual conference on Computational learning theory (1998)
Ghani, R.: Combining labeled and unlabeled data for multiclass text categorization. In: International Conference on Machine Learning (2002)
Nigam, K., McCallum, A.K., Thrun, S., Mitchell, T.: Text classification from labeled and unlabeled documents using EM. Machine learning 39 (2000)
Liu, B., Lee, W., Yu, P., Li, X.: Partially Supervised Classification of Text Documents. In: International Conference on Machine Learning, pp. 387–394 (2002)
Li, X., Liu, B.: Learning to Classify Texts Using Positive and Unlabeled Data. In: International joint Conference on Artificial Intelligence, pp. 587–594 (2003)
Yu, H., Han, J., Chang, K.C.-C.: PEBL: Positive Example Based Learning for Web Page Classification Using SVM. In: Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 239–248 (2002)
Fung, G.P.C., Yu, J.X., Lu, H., Yu, P.S.: Text Classification without Negative Examples. Proc. 21st Int’l Conf. Data Engineering (2005)
Yu, H., Han, J.: PEBL: Web Page Classification without Negative Examples. IEEE Trans. Knowledge and Data Engineering (2004)
Li, X., Liu, B.: Learning from Positive and Unlabeled Examples with Different Data Distributions. In: Gama, J., Camacho, R., Brazdil, P.B., Jorge, A.M., Torgo, L. (eds.) ECML 2005. LNCS, vol. 3720, pp. 218–229. Springer, Heidelberg (2005)
Fung, G.P.C., et al.: Text Classification without Negative Examples Revisit. IEEE Transactions on Knowledge and Data Engineering 18(1), 6–20 (2006)
Li, X., Liu, B., Ng, S.-K.: Learning to Classify Documents with Only a Small Positive Training Set. In: The European Conference on Machine Learning, pp. 201–213 (2007)
McCallum, A., Nigam, K.: Text classification by bootstrapping with keywords, EM and shrinkage. In: ACL Workshop on Unsupervised Learning in Natural Language Processing (1999)
Liu, B., Li, X., Lee, W.S., Yu, P.S.: Text Classification by Labeling Words. In: Proc. 19th National Conference on Artificial Intelligence (2004)
Ko, Y., Seo, J.: Text classification from unlabeled documents with bootstrapping and feature projection techniques. Information Processing and Management (2009)
Qiu, Q., Zhang, Y., Zhu, J.: Build a text classifier by a keyword and unlabeled documents. In: Pacific-Asia Conference on Knowledge Discovery and Data Mining (2009)
Wang., P., Hu, J., Zeng, H.J., Chen, Z.: Using Wikipedia knowledge to improve text classification. In: Knowledge information System (2008)
Wang., P., Hu, J., Zeng, H.J., Chen, L.: Improving Text Classification By Using Encyclopedia Knowledge. In: IEEE International Conference on Data Mining (2007)
Medelyan, O., Milne, D.: Augmenting domain-specific thesauri with knowledge from Wikipedia. In: Proceedings of the NZ Computer Science Research Student Conference, Christchurch, NZ (2008)
Milne, D., Medelyan, O., Witten, I.H.: Mining domain-specific thesauri from Wikipedia: A case study. In: Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence (2006)
Barbara, D., Domeniconi, C., Kang, N.: Mining Relevant Text from Unlabeled Documents. In: Proceedings of the Third IEEE International Conference on Data Mining (2003)
McCallum, A., Nigam, K.: A comparison of event models for naive bayes text classification. In: AAAI 1998 workshop on learning for text categorization (1998)
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
Qiu, Q., Zhang, Y., Zhu, J., Qu, W. (2009). Building a Text Classifier by a Keyword and Wikipedia Knowledge. In: Huang, R., Yang, Q., Pei, J., Gama, J., Meng, X., Li, X. (eds) Advanced Data Mining and Applications. ADMA 2009. Lecture Notes in Computer Science(), vol 5678. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03348-3_28
Download citation
DOI: https://doi.org/10.1007/978-3-642-03348-3_28
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-03347-6
Online ISBN: 978-3-642-03348-3
eBook Packages: Computer ScienceComputer Science (R0)