Second order features for maximising text classification performance

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 2167, Issue , 2001, Pages 419-430

  Raskutti, Bhavani ^a   Ferrá, Herman ^a   Kowalczyk, Adam ^a  

^a TELSTRA RESEARCH LABORATORIES   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION (OF INFORMATION); MACHINE LEARNING; NEAREST NEIGHBOR SEARCH; SUPPORT VECTOR MACHINES;

BASELINE ACCURACY; K NEAREST NEIGHBOURS (K-NN); N-GRAMS; SECOND-ORDER FEATURES; SVM CLASSIFIERS; TEXT CLASSIFICATION; TEXT CORPORA; WORD-PAIRS;

TEXT PROCESSING;

EID: 84948187054     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-44795-4_36     Document Type: Conference Paper

References (19)

1. C. Apte, F. Damerau, and S. M. Weiss. Text Mining with Decision Trees and Decision Rules. In Conference on Automated Learning and Discovery, 1998
2. J. E. Burnett, D. Cooper, M. F. Lynch, P. Willett, and M. Wycherley. Document Retrieval Experiments Using Indexing Vocabularies of Varying Size. I. Variety Generation Symbols Assigned to the Fronts of Index Terms. Journal of Documen-tation, 35(3):197-206, (1979)
3. W. B. Cavnar. N-gram-based text filtering for TREC-2. In Proceedings for Second Text Retrieval Conference (TREC-2), pages 200-215. NIST Special Publication, 1993
4. W. B. Cavnar and J. M. Trenkle. N-gram-based text Categorization. In Proceedings of Third Annual Symposium on Document Analysis and Information Retrieval, 1994
5. N. Christianini and J. Shawe-Taylor. Support Vector Machines and other Kernel Based Methods. Cambridge University Press, 2000
6. J. D. Cohen. Highlights: Language-and domain-independent automatic indexing terms for abstracting. Journal of the American Society for Information Science, 46(3):162-174, (1995)
7. S. Dumais, J. Platt, D. Heckerman, and M. Sahami. Inductive Learning Algorithms and Representations for Text Categorization. In Seventh International Conference on Information and Knowledge Management, 1998
8. J. Furnkranz. A Study Using n-gram Features for Text Categorization. Technical report, Austrian Reserach Institute for Artificial Intelligence, 1998
9. J. Furnkranz, T. Mitchell, and E. Rilò. A Case Study in Using Linguistic Phrases for Text Categorization on the WWW. In In AAAI-98 Workshop on Learning for Text Categorization, 1998
10. T. Joachims. Text Categorization with Support Vector Machines: Learning with Many Relevant Features. In Proceedings of the Tenth European Conference on Machine Learning ECML98, 1998
11. K. Kukich. Technique for automatically correcting words in text. ACM Computing Surveys, 24:377-439, (1992)
12. D. Lewis. Evaluating Text Categorization. In Proceedings of the Speech and Natural Language Workshop, 1991
13. D. Lewis. An Evaluation of Phrasal and Clustered Representations on a Text Categorization Task. In Proceedings of the Fifteenth International ACM SIGIR Conference on Research and Development in Information Retrieval, 1992
14. D. Lewis. Feature selection and feature extraction for text categorization. In Proceedings of the Speech and Natural Language Workshop. Defense Advanced Re-search Projects Agency, 1992
15. D. Mladenic and M. Grobelnik. Word Sequences as Features in Text Learning. In In Seventeenth Electrotechnical and Computer Science Conference, 1998
16. S. Scott and S. Matwin. Feature Engineering for Text Classification. In Proceedings of the Sixteenth International Conference on Machine Learning, 1999
17. V. Vapnik. Statistical Learning theory. Wiley, 1998
18. S. M. Weiss, C. Apte, F. Damerau, D.E. Johnson, F. J. Oles, T. Goetz, and T. Hampp. Maximizing Text-Mining Performance. IEEE Intelligent Systems, 14(4), (1999)
19. Y. Yang and J. O. Pedersen. A Comparative Study on Feature Selection in Text Categorization. In Proceedings of the Fourteenth International Conference on Machine Learning, 1997.