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Volumn 1, Issue , 2009, Pages 481-492

Exploiting semantic constraints for estimating supersenses with CRFs

Author keywords

[No Author keywords available]

Indexed keywords

COARSE-GRAINED; CONDITIONAL RANDOM FIELD; LABELED TRAINING DATA; ONTOLOGY CONCEPTS; SEMANTIC CONSTRAINTS; SEMANTIC INTERPRETATION; TRAINING DATA; TRAINING EXAMPLE; UNLABELED DATA; WORD SENSE; WORDNET;

EID: 72849120502     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (11)

References (25)
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    • Remco R. Bouckaert and Eibe Frank. Evaluating the replicability of significance tests for comparing learning algorithms. In Advances in Knowledge Discovery and Data Mining, 8th Pacific-Asia Conference, PAKDD 2004, Sydney, Australia, May 26-28, 2004, Proceedings, volume 3056 of Lecture Notes in Computer Science. Springer, 2004.
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    • Fu Cai, J.1    Wee, S.2    Yee, W.3
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    • Yee, S.1    Tou Ng, H.2    Zhong, Z.3
  • 8
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    • Ciaramita, M.1    Hofmann, T.2    Johnson, M.3
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    • Discriminative training methods for hidden markov models: Theory and experiments with perceptron algorithms
    • M. Collins. Discriminative training methods for hidden markov models: Theory and experiments with perceptron algorithms. In Proceedings of EMNLP 2002, pages 1-8, 2002.
    • (2002) Proceedings of EMNLP , pp. 1-8
    • Collins, M.1
  • 13
    • 0002487107 scopus 로고    scopus 로고
    • Word sense disambiguation: The state of the art
    • Nancy Ide and Jean Veronis. Word sense disambiguation: The state of the art. Computational Linguistics, 24:1-24, 1998.
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    • Ide, N.1    Veronis, J.2
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    • Conditional random fields: Probabilistic models for segmenting and labeling sequence data
    • John D. Lafferty, Andrew McCallum, and Fernando C. N. Pereira. Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In ICML, pages 282-289, 2001.
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.