Word sense disambiguation for spam filtering

Electronic Commerce Research and Applications

Volumn 11, Issue 3, 2012, Pages 290-298

  Laorden, Carlos ^a   Santos, Igor ^a   Sanz, Borja ^a   Alvarez, Gonzalo ^b   Bringas, Pablo G ^a  

^a UNIVERSITY OF DEUSTO   (Spain)

^b Consejo Superior de Investigaciones Científicas   (Spain)

Author keywords

Computer security; Secure e commerce; Spam filtering; Word sense disambiguation

Indexed keywords

MACHINE-LEARNING; PHISHING; PRE-PROCESSING STEP; SPAM FILTERING; SPAM MESSAGES; STATISTICAL REPRESENTATIONS; WORD SENSE DISAMBIGUATION;

COMPUTER VIRUSES; ELECTRONIC MAIL; LEARNING ALGORITHMS; LEARNING SYSTEMS; SECURITY OF DATA; SECURITY SYSTEMS; SEMANTICS;

INTERNET;

EID: 84862894504     PISSN: 15674223     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.elerap.2011.11.004     Document Type: Article

References (75)

1. E. Agirre, and P. Edmonds Word Sense Disambiguation: Algorithms and Applications 2007 Springer Publishing Company, Incorporated
2. Allan, J.; Carbonell, J.; Doddington, G.; Yamron, J.; Yang, Y.; et al. Topic detection and tracking pilot study: Final report. In Proceedings of the DARPA Broadcast News Transcription and Understanding Workshop, Vol. 1998, Citeseer, 1998.
3. S. Amari, and S. Wu Improving support vector machine classifiers by modifying kernel functions Neural Networks 12 6 1999 783 789
4. Androutsopoulos, I.; Koutsias, J.; Chandrinos, K.; Paliouras, G.; Spyropoulos, C. An evaluation of naive bayesian anti-spam filtering. In Proceedings of the Workshop on Machine Learning in the New Information Age, 2000, 9-17.
5. Androutsopoulos, I.; Koutsias, J.; Chandrinos, K.; Spyropoulos, C. An experimental comparison of naive Bayesian and keyword-based anti-spam filtering with personal e-mail messages. In Proceedings of the 23rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2000, 160-167.
6. Androutsopoulos, I.; Paliouras, G.; Karkaletsis, V.; Sakkis, G.; Spyropoulos, C.; Stamatopoulos, P. Learning to filter spam e-mail: A comparison of a naive bayesian and a memory-based approach. In Proceedings of the Machine Learning and Textual Information Access Workshop of the 4th European Conference on Principles and Practice of Knowledge Discovery in Databases, 2000.
7. Y. Bar-Hillel The Present Status of Automatic Translation of Languages 1 1960 91 163
8. M. Bates, and R. Weischedel Challenges in Natural Language Processing 1993 Cambridge Univ Pr
9. C. Bishop Pattern Recognition and Machine Learning 2006 Springer New York
10. Blanzieri, E.; Bryl, A. Instance-based spam filtering using SVM nearest neighbor classifier. In Proceedings of FLAIRS-20, 2007, 441-442.
11. A. Bratko, B. Filipič, G. Cormack, T. Lynam, and B. Zupan Spam filtering using statistical data compression models The Journal of Machine Learning Research 7 2006 2673 2698
12. L. Breiman Random forests Machine Learning 45 1 2001 5 32
13. J. Carpinter, and R. Hunt Tightening the net: a review of current and next generation spam filtering tools Computers & Security 25 8 2006 566 578
14. Carreras, X.; Chao, I.; Padró, L.; Padró, M. Freeling: An open-source suite of language analyzers. In Proceedings of the 4th LREC, Vol. 4, 2004.
15. Carreras, X.; Márquez, L. Boosting trees for anti-spam email filtering. In Proceedings of RANLP-01, 4th International Conference on Recent Advances in Natural Language Processing, Citeseer, 2001, 58-64.
16. Carreras, X.; Padró, L. A flexible distributed architecture for natural language analyzers. In Proceedings of the LREC, Vol. 2, 2002.
17. Cashell, B.; Jackson, W. D.; Jickling, M.; Webel, B. The economic impact of cyber-attacks. Congressional Research Service, Library of Congress, 2004.
18. Cooper, G. F.; Herskovits, E. A bayesian method for constructing bayesian belief networks from databases. In Proceedings of the 7th Conference on Uncertainty in Artificial Intelligence, 1991.
19. Cormack, G.; 2007. TREC 2007 spam track overview. In Sixteenth Text Retrieval Conference (TREC-2007), 1991.
20. H. Drucker, D. Wu, and V. Vapnik Support vector machines for spam categorization IEEE Transactions on Neural Networks 10 5 1999 1048 1054
21. C. Fellbaum WordNet: An Electronic Lexical Database 1998 MIT press Cambridge, MA
22. Garner, S. Weka: The Waikato environment for knowledge analysis. In Proceedings of the New Zealand Computer Science Research Students Conference, 1995, 57-64.
23. Geiger, D.; Goldszmidt, M.; Provan, G.; Langley, P.; Smyth, P. Bayesian network classifiers. In Machine Learning, 1997, 131-163.
24. Gonzalo, J.; Penas, A.; Verdejo, F. Lexical ambiguity and information retrieval revisited. In Proceedings of EMNLP/VLC, Vol. 99, 1999.
25. Gopal, R.; Tripathi, A.; Walter, Z. Economic issues in advertising via e-mail: role for a trusted third party? Marketing, 2011, 1pp.
26. S. Hansell Diverging Estimates of the Costs of Spam 2003 New York Times
27. S. Heron Technologies for spam detection Network Security 2009 1 2009 11 15
28. Holmes, G.; Donkin, A.; Witten, I. H. Weka: a machine learning workbench, 1994, 357-361.
29. N. Ide, and J. Véronis Word sense disambiguation: the state of the art Computational Linguistics 24 1998 1 40
30. T. Jagatic, N. Johnson, M. Jakobsson, and F. Menczer Social phishing Communications of the ACM 50 10 2007 94 100
31. J. Jung, and E. Sit An empirical study of spam traffic and the use of DNS black lists Proceedings of the 4th ACM SIGCOMM Conference on Internet Measurement 2004 ACM New York, NY, USA 370 375
32. C. Karlberger, G. Bayler, C. Kruegel, and E. Kirda Exploiting redundancy in natural language to penetrate bayesian spam filters WOOT '07: Proceedings of the First USENIX Workshop on Offensive Technologies 2007 USENIX Association Berkeley, CA, USA 1 7
33. J. Kent Information gain and a general measure of correlation Biometrika 70 1 1983 163
34. Kohavi, R. A study of cross-validation and bootstrap for accuracy estimation and model selection. In International Joint Conference on Artificial Intelligence, Vol. 14, 1995, 1137-1145.
35. Kołcz, A.; Chowdhury, A.; Alspector, J. The impact of feature selection on signature-driven spam detection. In Proceedings of the 1st Conference on Email and Anti-Spam (CEAS-2004), 2004.
36. Krovetz, B. On the importance of word sense disambiguation for information retrieval. In Proceedings of LREC Workshop on Creating and Using Semantics for Information Retrieval and Filtering, Las Palmas, Citeseer, 2002.
37. Krovetz, R. Homonymy and polysemy in information retrieval. In Proceedings of the eighth conference on European chapter of the Association for Computational Linguistics. Association for Computational Linguistics, 1997, 72-79.
38. R. Krovetz, and W. Croft Lexical ambiguity and information retrieval ACM Transactions on Information Systems (TOIS) 10 2 1992 115 141
39. Leung, C.; Liang, Z. An analysis of the impact of phishing and anti-phishing related announcements on market value of global firms. HKU Theses Online (HKUTO), 2009.
40. D. Lewis Naive (Bayes) at forty: the independence assumption in information retrieval Lecture Notes in Computer Science 1398 1998 4 18
41. Lin, H.; Lin, C. A study on sigmoid kernels for SVM and the training of non-PSD kernels by SMO-type methods. Tech. rep.; National Taiwan University, 2003.
42. J. Lovins Development of a stemming algorithm Mechanical Translation and Computational Linguistics 11 1 1968 22 31
43. Mallery, J. C. Thinking about foreign policy: finding an appropriate role for artificially intelligent computers. Master's thesis, M.I.T. Political Science Department, 1988.
44. D. Mavroeidis, G. Tsatsaronis, M. Vazirgiannis, M. Theobald, and G. Weikum Word sense disambiguation for exploiting hierarchical thesauri in text classification Knowledge Discovery in Databases: PKDD 2005 2005 181 192
45. Mihalcea, R.; Csomai, A. Senselearner: Word sense disambiguation for all words in unrestricted text. In: Proceedings of the ACL 2005 on Interactive Poster and Demonstration Sessions. Association for Computational Linguistics, 2005, 56.
46. Miller, G.; Leacock, C.; Tengi, R.; Bunker, R. A semantic concordance. In Proceedings of the workshop on Human Language Technology. Association for Computational Linguistics, 1993, 303-308.
47. Mishne, G.; Carmel, D.; Lempel, R. Blocking blog spam with language model disagreement. In Proceedings of the 1st International Workshop on Adversarial Information Retrieval on the Web (AIRWeb), 2005, 1-6.
48. N. Mostafa Raad, G. Alam, B. Zaidan, and A. Zaidan Impact of spam advertisement through e-mail: a study to assess the influence of the anti-spam on the e-mail marketing African Journal of Business Management 4 11 2010 2362 2367
49. R. Navigli Word sense disambiguation: a survey ACM Computing Surveys 41 2 2009 1 69
50. B. Nelson, M. Barreno, F. Jack Chi, A. Joseph, B. Rubinstein, U. Saini, C. Sutton, J. Tygar, and K. Xia Misleading learners: co-opting your spam filter Machine Learning in Cyber Trust 2009 17 51
51. J. Platt Sequential minimal optimization: a fast algorithm for training support vector machines Advances in Kernel Methods-Support Vector Learning 208 1999
52. J. Quinlan Induction of decision trees Machine Learning 1 1 1986 81 106
53. J. Quinlan C4. 5 Programs for Machine Learning 1993 Morgan Kaufmann Publishers
54. Ramachandran, A.; Dagon, D.; Feamster, N. Can DNS-based blacklists keep up with bots. In Conference on Email and Anti-Spam, Citeseer, 2006.
55. S.J. Russell, and Norvig Artificial Intelligence: A Modern Approach Second ed. 2003 Prentice Hall
56. Sahami, M.; Dumais, S.; Heckerman, D.; Horvitz, E. A Bayesian approach to filtering junk e-mail. In Learning for Text Categorization: Papers from the 1998 Workshop, Vol. 62. AAAI Technical Report WS-98-05, Madison, Wisconsin, 1998, 98-05.
57. G. Sakkis, I. Androutsopoulos, G. Paliouras, V. Karkaletsis, C. Spyropoulos, and P. Stamatopoulos A memory-based approach to anti-spam filtering for mailing lists Information Retrieval 6 1 2003 49 73
58. G. Salton, and M. McGill Introduction to Modern Information Retrieval 1983 McGraw-Hill New York
59. G. Salton, A. Wong, and C. Yang A vector space model for automatic indexing Communications of the ACM 18 11 1975 613 620
60. M. Sanderson Word sense disambiguation and information retrieval SIGIR '94: Proceedings of the 17th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 1994 Springer-Verlag New York, Inc New York, NY, USA 142 151
61. I. Santos, C. Laorden, B. Sanz, and P.G. Bringas Enhanced topic-based vector space model for semantics-aware spam filtering Expert Systems With Applications 39 1 2012 437 444 10.1016/j.eswa.2011.07.034
62. Schneider, K. A comparison of event models for Naive Bayes anti-spam e-mail filtering. In Proceedings of the 10th Conference of the European Chapter of the Association for Computational Linguistics, 2003, 307-314.
63. Sculley, D.; Wachman, G. Relaxed online SVMs for spam filtering. In Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2007, 415-422.
64. F. Sebastiani Machine learning in automated text categorization ACM Computing Surveys (CSUR) 34 1 2002 1 47
65. A. Seewald An evaluation of naive Bayes variants in content-based learning for spam filtering Intelligent Data Analysis 11 5 2007 497 524
66. Y. Singh, A. Kaur, and R. Malhotra Comparative analysis of regression and machine learning methods for predicting fault proneness models International Journal of Computer Applications in Technology 35 2 2009 183 193
67. Smith, K. T.; Smith, M.; Smith, J. L. Case studies of cybercrime and their impact on marketing activity and shareholder value. Academy of Marketing Studies Journal, 2011.
68. B. Üstün, W. Melssen, and L. Buydens Facilitating the application of support vector regression by using a universal Pearson VII function based kernel Chemometrics and Intelligent Laboratory Systems 81 1 2006 29 40
69. V. Vapnik The Nature of Statistical Learning Theory 2000 Springer
70. E. Voorhees Natural language processing and information retrieval Information Extraction 1999 724-724
71. Weaver, W. Translation. In W. N. Locke and A. D. Booth (eds.), Technology Press of MIT, Cambridge, MA, and John Wiley & Sons, New York, NY, 1949, 15-23.
72. W. Wilbur, and K. Sirotkin The automatic identification of stop words Journal of Information Science 18 1 1992 45 55
73. Y. Wilks, D. Fass, C. ming Guo, J.E. McDonaldl, T. Plate, and B.M. Slator Providing machine tractable dictionary tools Machine Translation 5 2 1990 99 154
74. H. Xu, and B. Yu Automatic thesaurus construction for spam filtering using revised back propagation neural network Expert Systems with Applications 37 1 2010 18 23
75. L. Zhang, J. Zhu, and T. Yao An evaluation of statistical spam filtering techniques ACM Transactions on Asian Language Information Processing (TALIP) 3 4 2004 243 269