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Volumn 151, Issue , 2006, Pages 219-226

Automated classification of congressional legislation

Author keywords

Institutions; Legislative activities; Support vector machines; SVMs; Text analysis; U.S. Congress

Indexed keywords

AUTOMATED CLASSIFICATION; LEGISLATIVE INDEXING VOCABULARY (LIV); TOPIC CODING SYSTEM;

EID: 34250736811     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1146598.1146660     Document Type: Conference Paper
Times cited : (36)

References (14)
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    • Cristianini, N., Shawe-Taylor, J., and Lodhi, H. Latent semantic kernels, in Brodley, C. and Danyluk, A. Proceedings of ICML-01, 18th International Conference on Machine Learning. (San Francisco, US, 2001), Morgan Kaufmann Publishers, pages 66-73.
  • 3
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    • Kappa Statistic is not Satisfactory for Assessing the Extent of Agreement Between Raters
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    • Gwet, K.1
  • 5
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    • Making Large-Scale SVM Learning Practical
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    • Joachims, T. Making Large-Scale SVM Learning Practical, in: Advances in Kernel Methods - Support Vector Learning, B. Schölkopf, C. Surges, and A. Smola (ed.), MIT Press, 1999.
    • (1999) Advances in Kernel Methods - Support Vector Learning
    • Joachims, T.1
  • 6
    • 34250717271 scopus 로고    scopus 로고
    • Kwon, N., Shulman, S.W., andHovy, E.H.. (Under review). Collective text analysis for eRulemaking. Proceedings of the Sixth National Conference on Digital Government Research. San Diego, CA.
    • Kwon, N., Shulman, S.W., andHovy, E.H.. (Under review). "Collective text analysis for eRulemaking." Proceedings of the Sixth National Conference on Digital Government Research. San Diego, CA.
  • 7
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    • Extracting policy positions from political texts using words as data
    • Laver, M., Benoit, K., and Garry, J. Extracting policy positions from political texts using words as data. In American Political Science Review 97(2).
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    • An algorithm for suffix stripping
    • Porter, M. F. An algorithm for suffix stripping. Program, 16(3):130-137.
    • Program , vol.16 , Issue.3 , pp. 130-137
    • Porter, M.F.1
  • 10
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    • Machine learning in automated text categorization
    • Sebastiani, F. Machine learning in automated text categorization. ACM Computing Surveys, 34(1).
    • ACM Computing Surveys , vol.34 , Issue.1
    • Sebastiani, F.1
  • 11
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    • Text categorization based on weighted inverse document frequency
    • Technical Report 94 TR0001, Department of Computer Science, Tokyo Institute of Technology
    • Tokunaga, T. and Iwayama, M. Text categorization based on weighted inverse document frequency. Technical Report 94 TR0001, Department of Computer Science, (Tokyo Institute of Technology, 1994).
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  • 13
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    • A re-examination of text categorization methods
    • November
    • Yang, Y. and Liu, X. 1999. A re-examination of text categorization methods. In Proceedings of SIGIR-99, November.
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.