Using feature construction to avoid large feature spaces in text classification

Proceedings of the 12th Annual Genetic and Evolutionary Computation Conference, GECCO '10

Volumn , Issue , 2010, Pages 1299-1306

  Mayfield, Elijah ^a   Penstein Rosé, Carolyn ^a  

^a Carnegie Mellon University   (United States)

Author keywords

Feature space design; Sentiment analysis; Text classification

Indexed keywords

ARBITRARY NUMBER; CLASSIFICATION ACCURACY; COMBINED FEATURES; CRITICAL PARTS; DATA SPARSITY; FEATURE CONSTRUCTION; FEATURE SPACE; MACHINE-LEARNING; PRE-PROCESSING STEP; PREDICTIVE POWER; SENTIMENT ANALYSIS; SOPHISTICATED MACHINES; TEXT CLASSIFICATION;

GENETIC PROGRAMMING; INFORMATION RETRIEVAL SYSTEMS; LEARNING SYSTEMS; MACHINE DESIGN;

TEXT PROCESSING;

EID: 77955900354     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1830483.1830714     Document Type: Conference Paper

References (26)

1. L. Araujo andJ. Pérez-Aguéra. Improving query expansion with stemming terms: A new genetic algorithm approach. In Evolutionary Computation in Combinatorial Optimization, 2008.
2. S. Argamon, C. Whitelaw, P. Chase, S. Hota, N. Garg, andS. Levitan. Stylistic text classification using functional lexical features. Journal of the American Society for Information Science and Technology, 2007.
3. S. Arora andE. Nyberg. Interactive annotation learning with indirect feature voting. In Proceedings of NAACL-HLT, 2009.
4. J. Bala, K. D. Jong, J. Huang, H. Vafaie, andH. Wechsler. Using learning to facilitate the evolution of features for recognizing visual concepts. In Evolutionary Computation, 1997.
5. M. Collins andN. Duffy. Convolution kernels for natural language. In Advances in Neural Information Processing Systems 14, 2001.
6. O. Cordon, E. Herrera-Viedma, C. Lopez-Pujalte, M. J. Luque, andC. Zarco. A review on the application of evolutionary computation to information retrieval. 2003.
7. J. Furnkranz, T. Mitchell, M. Mitchell, andE. Riloff. A case study in using linguistic phrases for text categorization on the www. In AAAI/ICML Workshop on Learning for Text Categorization, 1998.
8. L. Hirsch, R. Hirsch, andM. Saeedi. Evolving lucene search queries for text classification. In Proceedings of GECCO, 2007.
9. T. Joachims. Making large-scale support vector machine learning practical, 1998.
10. M. Joshi andC. Penstein-Rosé. Generalizing dependency features for opinion mining. In Proceedings of the ACL-IJCNLP, 2009.
11. K. Krawiec. Genetic programming-based construction of features for machine learning and knowledge discovery tasks. In Genetic Programming and Evolvable Machines, 2002.
12. S. Luke. Ecj: A java-based evolutionary computation research system. http://cs.gmu.edu/eclab/projects/ecj/.
13. J. Martin andP. White. The Language of Evaluation: The Appraisal Framework. 2005.
14. A. Moschitti. Efficient convolution kernels for dependency and constituent syntactic trees. In Proceedings of the 17th European Conference on Machine Learning, 2006.
15. F. Otero, M. Silva, A. Freitas, andJ. Nievola. Genetic programming for attribute construction in data mining. In Proceedings of GECCO, 2002.
16. B. Pang andL. Lee. A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts. In Proceedings of the ACL, pages 271-278, 2004.
17. B. Pang andL. Lee. Opinion Mining and Sentiment Analysis. 2008.
18. J. Rennie, L. Shih, J. Teevan, andD. Karger. Tackling the poor assumptions of naive bayes text classifiers. In Machine Learning, 2003.
19. O. Ritthoff, R. Klinkenberg, S. Fischer, and I. Mierswa. A hybrid approach to feature selection and generation using an evolutionary algorithm. 2002.
20. C. Rose. A genetic programming approach for robust language interpretation. In Advances in Genetic Programming, Volume 3, 1999.
21. M. Smith andL. Bull. Genetic programming with a genetic algorithm for feature construction and selection. In Genetic Programming and Evolvable Machines, 2005.
22. M. P. Smith andM. Smith. The use of genetic programming to build boolean queries for text retrieval through relevance feedback. 2003.
23. K. Tsutsumi, K. Shimada, andT. Endo. Movie review classification based on a multiple classifier. 2007.
24. I. Witten andE. Frank. Data mining: practical machine learning tools and techniques with Java implementations. 2002.
25. Y. Yang andJ. Pedersen. A comparative study on feature selection in text. 1997.
26. O. F. Zaidan andJ. Eisner. Using "annotator rationales" to improve machine learning for text categorization. In Proceedings of NAACL-HLT, 2007.