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Term Network Approach for Transductive Classification

  • Conference paper
Computational Linguistics and Intelligent Text Processing (CICLing 2015)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9042))

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Abstract

Transductive classification is a useful way to classify texts when just few labeled examples are available. Transductive classification algorithms rely on term frequency to directly classify texts represented in vector space model or to build networks and perform label propagation. Related terms tend to belong to the same class and this information can be used to assign relevance scores of terms for classes and consequently the labels of documents. In this paper we propose the use of term networks to model term relations and perform transductive classification. In order to do so, we propose (i) different ways to generate term networks, (ii) how to assign initial relevance scores for terms, (iii) how to propagate the relevance scores among terms, and (iv) how to use the relevance scores of terms in order to classify documents. We demonstrate that transductive classification based on term networks can surpass the accuracies obtained by transductive classification considering texts represented in other types of networks or vector space model, or even the accuracies obtained by inductive classification. We also demonstrated that we can decrease the size of term networks through feature selection while keeping classification accuracy and decreasing computational complexity.

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References

  1. Aggarwal, C.C., Zhao, P.: Towards graphical models for text processing. Knowledge & Information Systems 36(1), 1–21 (2013)

    Article  Google Scholar 

  2. Angelova, R., Weikum, G.: Graph-based text classification: learn from your neighbors. In: Proc. Special Interest Group on Information Retrieval Conference, pp. 485–492. ACM (2006)

    Google Scholar 

  3. Blum, A., Mitchell, T.: Combining labeled and unlabeled data with co-training. In: Proc. Conf. Computational Learning Theory, pp. 92–100. ACM (1998)

    Google Scholar 

  4. Dhillon, I.S.: Co-clustering documents and words using bipartite spectral graph partitioning. In: Proc. Int. Conf. Knowledge Discovery and Data Mining, pp. 269–274. ACM (2001)

    Google Scholar 

  5. Forman, G.: 19MclassTextWc dataset (2006), http://sourceforge.net/projects/weka/files/datasets/text-datasets/19MclassTextWc.zip/download

  6. García, S., Fernández, A., Luengo, J., Herrera, F.: Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: Experimental analysis of power. Information Sciences 180(10), 2044 (2010)

    Article  Google Scholar 

  7. Geng, L., Hamilton, H.J.: Interestingness measures for data mining: A survey. ACM Computing Surveys 38(3) (2006)

    Google Scholar 

  8. Ji, M., Sun, Y., Danilevsky, M., Han, J., Gao, J.: Graph regularized transductive classification on heterogeneous information networks. In: Balcázar, J.L., Bonchi, F., Gionis, A., Sebag, M. (eds.) ECML PKDD 2010, Part I. LNCS, vol. 6321, pp. 570–586. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  9. Jiang, C., Coenen, F., Sanderson, R., Zito, M.: Text classification using graph mining-based feature extraction. Knowledge-Based Systems 23(4), 302–308 (2010)

    Article  Google Scholar 

  10. Joachims, T.: Transductive inference for text classification using support vector machines. In: Proc. Int. Conf. on Machine Learning, pp. 200–209 (1999)

    Google Scholar 

  11. Marcacini, R.M., Rezende, S.O.: Incremental construction of topic hierarchies using hierarchical term clustering. In: Int. Conf. Software Engineering & Knowledge Engineering, pp. 553–558 (2010)

    Google Scholar 

  12. Markov, A., Last, M., Kandel, A.: Model-based classification of web documents represented by graphs. In: Proc. Workshop on Web Mining and Web Usage Analysis, pp. 1–8 (2006)

    Google Scholar 

  13. Matsuo, Y., Sakaki, T., Uchiyama, K., Ishizuka, M.: Graph-based word clustering using a web search engine. In: Prof. Conf. on Empirical Methods in Natural Language Processing, pp. 542–550. ACL (2006)

    Google Scholar 

  14. Mihalcea, R., Tarau, P.: TextRank: Bringing order into texts. In: Proc. Conf. Empirical Methods in Natural Language Processing (2004)

    Google Scholar 

  15. Mishra, M., Huan, J., Bleik, S., Song, M.: Biomedical text categorization with concept graph representations using a controlled vocabulary. In: Proc. Int. Workshop on Data Mining in Bioinformatics, pp. 26–32. ACM (2012)

    Google Scholar 

  16. Nigam, K., McCallum, A.K., Thrun, S., Mitchell, T.: Text classification from labeled and unlabeled documents using EM. Machine Learning 39(2/3), 103–134 (2000)

    Article  MATH  Google Scholar 

  17. Page, L., Brin, S., Motwani, R., Winograd, T.: The PageRank citation ranking: Bringing order to the web. Technical Report 1999-66, Stanford University (November 1998), http://ilpubs.stanford.edu:8090/422/

  18. Porter, M.F.: An algorithm for suffix stripping. Readings in Information Retrieval 14(3), 130–137 (1980)

    Google Scholar 

  19. Rossi, R.G., de Andrade Lopes, A., de Paulo Faleiros, T., Rezende, S.O.: Inductive model generation for text classification using a bipartite heterogeneous network. Journal of Computer Science and Technology 3(29), 361–375 (2014)

    Article  Google Scholar 

  20. Rossi, R.G., Lopes, A.A., Rezende, S.O.: A parameter-free label propagation algorithm using bipartite heterogeneous networks for text classification. In: Proc. Symposium on Applied Computing. ACM (2014)

    Google Scholar 

  21. Rossi, R.G., Marcacini, R.M., Rezende, S.O.: Benchmarking text collections for classification and clustering tasks. Tech. Rep. 395, Institute of Mathematics and Computer Sciences - University of Sao Paulo (2013), http://www.icmc.usp.br/CMS/Arquivos/arquivos_enviados/BIBLIOTECA_113_RT_395.pdf

  22. Salton, G.: Automatic text processing: the transformation, analysis, and retrieval of information by computer. Addison-Wesley Longman Publishing Co., Inc. (1989)

    Google Scholar 

  23. Schenker, A., Last, M., Bunke, H., Kandel, A.: Classification of web documents using a graph model. In: Proc. Int. Conf. Document Analysis and Recognition, pp. 240–244. IEEE Computer Society (2003)

    Google Scholar 

  24. Sebastiani, F.: Machine learning in automated text categorization. ACM Computing Surveys 34(1), 1–47 (2002)

    Article  Google Scholar 

  25. Solé, R.V., Corominas-Murtra, B., Valverde, S., Steels, L.: Language networks: their structure, function, and evolution. Complexity 15(6), 20–26 (2010)

    Article  Google Scholar 

  26. Steyvers, M., Tenenbaum, J.B.: The large-scale structure of semantic networks: Statistical analyses and a model of semantic growth. Cognitive Science 29, 41–78 (2005)

    Article  Google Scholar 

  27. Tan, P.-N., Kumar, V., Srivastava, J.: Selecting the right interestingness measure for association patterns. In: Proc. Int. Conf. Knowledge Discovery and Data Mining, pp. 32–41. ACM (2002)

    Google Scholar 

  28. Tseng, Y.-H., Ho, Z.-P., Yang, K.-S., Chen, C.-C.: Mining term networks from text collections for crime investigation. Expert Systems with Applications 39(11), 10082–10090 (2012)

    Article  Google Scholar 

  29. Wang, W., Do, D.B., Lin, X.: Term graph model for text classification. In: Li, X., Wang, S., Dong, Z.Y. (eds.) ADMA 2005. LNCS (LNAI), vol. 3584, pp. 19–30. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  30. Weiss, S.M., Indurkhya, N., Zhang, T.: Fundamentals of Predictive Text Mining. Springer London Ltd. (2010)

    Google Scholar 

  31. Witten, I.H., Frank, E.: Data Mining: Practical machine learning tools and techniques, 2nd edn. Morgan Kaufmann (2005)

    Google Scholar 

  32. Yarowsky, D.: Unsupervised word sense disambiguation rivaling supervised methods. In: Proc. Annual Meeting on Association for Computational Linguistics, pp. 189–196. Association for Computational Linguistics (1995)

    Google Scholar 

  33. Zhou, D., Bousquet, O., Lal, T.N., Weston, J., Schölkopf, B.: Learning with local and global consistency. In: Advances in Neural Information Processing Systems, vol. 16, pp. 321–328 (2004)

    Google Scholar 

  34. Zhu, X., Ghahramani, Z., Lafferty, J.: Semi-supervised learning using gaussian fields and harmonic functions. In: Proc. Int. Conf. Machine Learning, pp. 912–919. AAAI Press (2003)

    Google Scholar 

  35. Zhu, X., Goldberg, A.B., Brachman, R., Dietterich, T.: Introduction to Semi-Supervised Learning. Morgan and Claypool Publishers (2009)

    Google Scholar 

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Authors and Affiliations

  1. Institute of Mathematics and Computer Science, University of So Paulo, So Carlos, SP, Brazil

    Rafael Geraldeli Rossi, Solange Oliveira Rezende & Alneu de Andrade Lopes

Authors
  1. Rafael Geraldeli Rossi
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  2. Solange Oliveira Rezende
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  3. Alneu de Andrade Lopes
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Correspondence to Rafael Geraldeli Rossi .

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Editors and Affiliations

  1. Centro de Investigación en Computación, Instituto Politécnico Nacional, Mexico DF, Mexico

    Alexander Gelbukh

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Rossi, R.G., Rezende, S.O., de Andrade Lopes, A. (2015). Term Network Approach for Transductive Classification. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2015. Lecture Notes in Computer Science(), vol 9042. Springer, Cham. https://doi.org/10.1007/978-3-319-18117-2_37

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  • DOI: https://doi.org/10.1007/978-3-319-18117-2_37

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-18116-5

  • Online ISBN: 978-3-319-18117-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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