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Volumn 2002-July, Issue , 2002, Pages 263-270

New ranking algorithms for parsing and tagging: Kernels over discrete structures, and the voted perceptron

Author keywords

[No Author keywords available]

Indexed keywords

LEARNING ALGORITHMS; NATURAL LANGUAGE PROCESSING SYSTEMS;

EID: 33646057547     PISSN: 0736587X     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (433)

References (21)
  • 1
    • 0000874557 scopus 로고
    • Theoretical Foundations of the Potential Function Method in Pattern Recognition Learning
    • Aizerman, M., Braverman, E., & Rozonoer, L. (1964). Theoretical Foundations of the Potential Function Method in Pattern Recognition Learning. In Automation and Remote Control, 25:821-837.
    • (1964) Automation and Remote Control , vol.25 , pp. 821-837
    • Aizerman, M.1    Braverman, E.2    Rozonoer, L.3
  • 3
    • 0009626003 scopus 로고    scopus 로고
    • What is the Minimal Set of Fragments that Achieves Maximal Parse Accuracy?
    • Bod, R. (2001). What is the Minimal Set of Fragments that Achieves Maximal Parse Accuracy? In Proceedings of ACL 2001.
    • (2001) Proceedings of ACL 2001
    • Bod, R.1
  • 9
    • 85045072379 scopus 로고    scopus 로고
    • Ranking Algorithms for Named-Entity Extraction: Boosting and the Voted Perceptron
    • Collins, M. (2002a). Ranking Algorithms for Named-Entity Extraction: Boosting and the Voted Perceptron. In Proceedings of ACL 2002.
    • (2002) Proceedings of ACL 2002
    • Collins, M.1
  • 10
    • 85127836544 scopus 로고    scopus 로고
    • Discriminative Training Methods for Hidden Markov Models: Theory and Experiments with the Perceptron Algorithm
    • Collins, M. (2002b). Discriminative Training Methods for Hidden Markov Models: Theory and Experiments with the Perceptron Algorithm. In Proceedings of EMNLP 2002.
    • (2002) Proceedings of EMNLP 2002
    • Collins, M.1
  • 12
    • 0033281425 scopus 로고    scopus 로고
    • Large Margin Classification using the Perceptron Algorithm
    • Freund, Y. & Schapire, R. (1999). Large Margin Classification using the Perceptron Algorithm. In Machine Learning, 37(3):277-296.
    • (1999) Machine Learning , vol.37 , Issue.3 , pp. 277-296
    • Freund, Y.1    Schapire, R.2
  • 17
    • 0040078058 scopus 로고    scopus 로고
    • The DOP estimation method is biased and inconsistent
    • Johnson, M. (2002). The DOP estimation method is biased and inconsistent. Computational Linguistics, 28, 71-76.
    • (2002) Computational Linguistics , vol.28 , pp. 71-76
    • Johnson, M.1
  • 19
    • 34249852033 scopus 로고
    • Building a large annotated corpus of english: The Penn treebank
    • Marcus, M., Santorini, B., & Marcinkiewicz, M. (1993). Building a large annotated corpus of english: The Penn treebank. Computational Linguistics, 19, 313-330.
    • (1993) Computational Linguistics , vol.19 , pp. 313-330
    • Marcus, M.1    Santorini, B.2    Marcinkiewicz, M.3
  • 21
    • 11144273669 scopus 로고
    • The Perceptron: A Probabilistic Model for Information Storage and Organization in the Brain
    • (Reprinted in Neurocomputing (MIT Press, 1998))
    • Rosenblatt, F. 1958. The Perceptron: A Probabilistic Model for Information Storage and Organization in the Brain. Psychological Review, 65, 386-408. (Reprinted in Neurocomputing (MIT Press, 1998).)
    • (1958) Psychological Review , vol.65 , pp. 386-408
    • Rosenblatt, F.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.