Statistical Bayesian learning for automatic Arabic text categorization

Journal of Computer Science

Volumn 7, Issue 1, 2011, Pages 39-45

  Al Salemi, Bassam ^a   Aziz, Mohd Juzaiddin Ab ^a  

^a UNIVERSITI KEBANGSAAN MALAYSIA   (Malaysia)

Author keywords

Arabic text categorization; Automatic text categorization; Bayesian learning; Feature selection (FS); Information gain (IG); Mutual information (MI); Odds ratio (OR)

Indexed keywords

EID: 79251506653     PISSN: 15493636     EISSN: None     Source Type: Journal    
DOI: 10.3844/jcssp.2011.39.45     Document Type: Article

References (25)

1. Al-Harbi, S., A. Ab, A. Al-Thubaity, M.S. Khorsheed and A. Al-Rajeh, 2008. automatic arabic text classification. 9es journées internationales d'analyse statistique des données textuelles, JADT, 08: 77-83.
2. Cavnar, W.B. and J.M. Trenkle, 1994. N-gram based text categorization. Proceeding of the 3rd Annual Symposium on Document Analysis and Information Retrieval, SDAIR-94
3. Chen, J., H. Huang, S. Tian and Y. Qu, 2009. feature selection for text classification with naïve bayes. expert systems with applications. Int. J., 36: 5432-5435. DOI: 10.1016/j.eswa.2008.06.054
4. Darwish, K. and D.W. Oard, 2002. CLIR Experiments at Maryland for TREC-2002: evidence combination for Arabic-English retrieval. TREC 2002. Gaithersburg: NIST, pp: 703-710.
5. Duwiri, R., 2006. Machine learning for Arabic text categorization. J. Am. Soc. Inform. Sci. Technol., 57: 1005-1010. DOI: 10.1002/asi.20360
6. Duwiri, R., 2007. Arabic Text categorization. Int. Arab J. Inform. Technol., 4: 152-131.
7. El-Kourdi, M., A. Bensaid and T. Rachidi, 2004. Automatic Arabic document categorization based on the Naïve-Bayes Algorithm. Proceeding of the Workshop on Computational Approaches to Arabic Script-Based Languages, COLING-2004, University of Geneva, Geneva, Switzerland.
8. Eyheramendy, S., D. Lewis and D. Madigan, 2003. On the naive bayes model for text categorization. Proceeding of the 9th International Workshop on Artificial Intelligence and Statistics, pp: 332-339. Key West, Florida.
9. Forman, G., 2003. An extensive empirical study of feature selection metrics for text classification. J. Mach. Learn. Res. ACM, 3: 1289-1305.
10. Fragoudis, D., D. Meretakis and S. Likothanassis, 2005. Best Terms: An efficient feature-selection algorithm for text categorization. Knowl. Inform. Syst. Spring Link, 8: 16-33. DOI: 10.1007/s10115-004-0177-2
11. Galavotti, L., F. Sebastiani and M. Simi, 2000. Experiments on the use of feature selection and negative evidence in automated text categorization. LNCS, Springer Link, 1923: 59-68. DOI: 10.1007/3-540-45268-0_6
12. Haraty, R.A. and O.E. Ariss, 2007. CASRA+: A colloquial Arabic speech recognition application. Am. J. Applied Sci., 4: 23-32. DOI: 10.3844/.2007.23.32
13. Harrag, F., E. El-Qawasmeh and P. Pichappan 2009. Improving Arabic text categorization using decision trees. Networked Digital Technol. IEEE, 110-115. DOI: 10.1109/NDT.2009.5272214
14. Kanaan, G., R. Al-Shalabi, S. Ghwanmeh and H. Al-Maadeed, 2009. A comparison of text-classification techniques applied to Arabic Text. J. Am. Soc. Inform. Sci. Technol., 60: 1836-1844. DOI: 10.1002/asi.20832
15. Khreisat, L., 2009. A machine learning approach for Arabic text classification using N-gram frequency statistics. J. Inform., 3: 72-77.
16. Kotsiantis, S.B., 2007. Supervised machine learning: A review of classification techniques. Informatica, 31: 249-268.
17. Larkey, L., L. Ballesteros and M. Connell, 2007. Light stemming for Arabic information retrieval. SPRING. Arabic computational morphology text. Speech Language Technol., 38: 221-243. DOI: 10.1007/978-1-4020-6046-5_12
18. McCallum, A. and K. Nigam, 1998. a comparison of event models for naive Bayes text classification. Proceeding of the AAAI-98 Workshop on Learning for Text Categorization.
19. Méndez, J.R., I. Cid, D. Glez-Peña, M. Rocha and F. Fdez-Riverola, 2008. A Comparative Impact Study Of Attribute Selection Techniques On Naïve Bayes Spam Filters. advances in data mining, medical applications, e-commerce. Marketing Theoretical Aspects LNCs Springer, 5077: 213-227, DOI: 10.1007/978-3-540-70720-2_17
20. Mesleh, A., 2008. Support vector machines based Arabic language text classification system. Feature Selection Comparative Study: Sobh, T. (Ed.). Advanced in Computer SND Information Science and Engineering, Springer Link, pp: 11-16. DOI: 10.1007/978-1-4020-8741-7_3
21. Mesleh, A.M.A., 2007. Chi square feature extraction based SVMs Arabic language text categorization system. J. Comput. Sci., 3: 430-435.
22. Sebastiani, F., 2002. Machine learning in automated text categorization. ACM Comput. Surveys (CSUR), 34: 1-47. DOI: 10.1145/505282.505283
23. Schneider, K., 2003. A comparison of event models for naive bayes anti-spam e-mail filtering. Proceeding of the 10th Conference on European Chapter of the Association for Computational Linguistics ACM, Budapest, Hungary, pp: 307-314.
24. Yang, Y., 1999. An Evaluation of Statistical Approaches to Text Categorization. Information Retrieval, Springer Link, 1: 69-90. DOI: 10.1023/A:1009982220290
25. Yang, Y. and J. Pedersen, 1997. A Comparative Study on Feature Selection in Text Categorization. In: Fisher, J.D.H., (Ed.), The Fourteenth International Conference on Machine Learning (ICML'97), Morgan Kaufmann, pp: 412-420.