Using the Web as corpus for self-training text categorization

Information Retrieval

Volumn 12, Issue 3, 2009, Pages 400-415

  Guzmán Cabrera, Rafael ^a,b   Montes Y Gómez, Manuel ^c   Rosso, Paolo ^b   Villaseñor Pineda, Luis ^c  

^a UNIVERSITY OF GUANAJUATO   (Mexico)

^b UNIVERSITAT POLITÈCNICA DE VALÈNCIA   (Spain)

^c INSTITUTO NACIONAL DE ASTROFÍSICA   (Mexico)

Author keywords

Authorship attribution; Self training; Semi supervised learning; Text categorization; Web as corpus

Indexed keywords

EID: 64749095041     PISSN: 13864564     EISSN: 15737659     Source Type: Journal    
DOI: 10.1007/s10791-008-9083-7     Document Type: Article

References (28)

1. Aas, K., & Eikvil, L. (1999). Text categorization: A survey. Tech. Rep. 941. Norwegian Computing Center.
2. Argamon, S., & Levitan, S. (2005). Measuring the usefulness of function words for authorship attribution. In Proceedings of ACH/ALLC Conference 2005.
3. Bekkerman, R., & Allan, J. (2004). Using bigrams in text categorization. Tech. Rep. IR-408. Center of Intelligent Information Retrieval, UMass Amherst.
4. C Chaski 2005 Who's at the keyboard: Authorship attribution in digital evidence investigations International Journal of Digital Evidence 4 1 1 13
5. NV Chawla N Japkowicz A Kotcz 2004 Editorial: Special issue on learning from imbalanced data sets SIGKDD Explorations 6 1 1 6
6. Coyotl-Morales, R. M., Villaseñor-Pineda, L., Montes-Y- Gómez, M., & Rosso, P. (2006). Authorship attribution using word sequences. In J. F. Martínez-Trinidad, J. A. Carrasco-Ochoa, & J. Kittler (Eds.), CIARP (Vol. 4225, pp. 844-853). Springer, Lecture Notes in Computer Science.
7. J Diederich J Kindermann E Leopold G Paass 2003 Authorship attribution with support vector machines Applied Intelligence 19 1/2 109 123
8. HO Hartley JNK Rao 1968 Classification and estimation in analysis of variance problems Review of the International Statistical Institute 36 2 141 147
9. DI Holmes 1994 Authorship attribution Computers and the Humanities 28 87 106
10. Hoste, V. (2005). Optimization issues in machine learning of coreference resolution. Ph.D. thesis, Faculteit Letteren en Wijsbegeerte, Universiteit Antwerpen, Belgium.
11. Joachims, T. (1999). Transductive inference for text classification using support vector machines. In Proceedings of the 16th International Conference on Machine Learning (pp. 200-209). San Francisco, CA: Morgan Kaufmann.
12. Kaster, A., Siersdorfer, S., & Weikum, G. (2005). Combining text and linguistic document representations for authorship attribution. In SIGIR Workshop: Stylistic Analysis of Text for Information Access (STYLE) (pp. 27-35).
13. A Kilgarriff G Grefenstette 2003 Introduction to the Special issue of the Web as Corpus Computational Linguistics 29 2 333 347
14. Malyutov, M. B. (2006). Authorship attribution of texts: A review. In R. Ahlswede, L. Bäumer, N. Cai, H. K. Aydinian, V. Blinovsky, C. Deppe, & H. Mashurian (Eds.), GTIT-C (Vol. 4123, pp. 362-380). Springer, Lecture Notes in Computer Science.
15. Moschitti, A., & Basili, R. (2004). Complex linguistic features for text classification: A comprehensive study. In S. McDonald & J. Tait (Eds.), Proceedings of the 26th European Conference on Information Retrieval (ECIR 2004) (Vol. 2997, pp. 181-196). Sunderland, UK: Springer, Lecture Notes in Computer Science.
16. K Nigam AK Mccallum S Thrun T Mitchell 2000 Text classification from labeled and unlabeled documents using EM Machine Learning 39 2/3 103 134
17. F Peng D Schuurmans S Wang 2004 Augmenting naive Bayes classifiers with statistical language models Information Retrieval 7 3-4 317 345
18. F Sebastiani 2002 Machine learning in automated text categorization ACM Computing Surveys 34 1 1 47
19. Seeger, M. (2000). Learning with labeled and unlabeled data. Tech. Rep. Edinburgh, UK: University of Edinburgh.
20. Smucker, M., Allan, J., & Carterette, B. (2007). A comparison of statistical significance tests for information retrieval evaluation. In Proceedings of the ACM Sixteenth Conference on Information and Knowledge Management (pp. 623-632).
21. Solorio, T. (2002). Using unlabeled data to improve classifier accuracy. Master's thesis, Computer Science Department, INAOE, Mexico.
22. E Stamatatos N Fakotakis G Kokkinakis 2001 Computer-based authorship attribution without lexical measures Computers and the Humanities 35 193 214
23. Witten, I. H., & Frank, E. (1999). Data mining: Practical machine learning tools and techniques with Java implementations. Morgan Kaufmann.
24. Yu, B. (2006). An evaluation of text classification methods for literary study. Ph.D. thesis, Champaign, IL, USA.
25. Zelikovitz, S., & Hirsh, H. (2002). Integrating background knowledge into nearest-neighbor text classification. In S. Craw & A. D. Preece (Eds.), ECCBR (Vol. 2416, pp. 1-5). Springer, Lecture Notes in Computer Science.
26. Zelikovitz, S., & Kogan, M. (2006). Using web searches on important words to create background sets for LSI classification. In G. Sutcliffe & R. Goebel (Eds.), FLAIRS Conference (pp. 598-603). AAAI Press.
27. Zhao, Y., & Zobel, J. (2005). Effective and scalable authorship attribution using function words. In G. G. Lee, A. Yamada, H. Meng, & S. H. Myaeng (Eds.), AIRS (Vol. 3689, pp. 174-189). Springer, Lecture Notes in Computer Science.
28. Zhu, X. (2005). Semi-supervised learning literature survey. Tech. Rep. Computer Sciences, University of Wisconsin-Madison.