메뉴 건너뛰기




Volumn , Issue , 2008, Pages 496-504

Combining EM training and the MDL principle for an automatic verb classification incorporating selectional preferences

Author keywords

[No Author keywords available]

Indexed keywords

CLASS MODELS; EM ALGORITHMS; EM TRAINING; MDL PRINCIPLE; SELECTIONAL PREFERENCES; SEMANTIC CLASS; TWO-DIMENSION;

EID: 84859909840     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (27)

References (27)
  • 2
    • 0001862769 scopus 로고
    • An inequality and associated maximization technique in statistical estimation for probabilistic functions of Markov processes
    • Leonard E. Baum. 1972. An Inequality and Associated Maximization Technique in Statistical Estimation for Probabilistic Functions of Markov Processes. Inequalities, III:1-8.
    • (1972) Inequalities , vol.3 , pp. 1-8
    • Baum, L.E.1
  • 7
    • 0040669675 scopus 로고    scopus 로고
    • Class-based probability estimation using a semantic hierarchy
    • Stephen Clark and David Weir. 2002. Class-Based Probability Estimation using a Semantic Hierarchy. Computational Linguistics, 28(2):187-206.
    • (2002) Computational Linguistics , vol.28 , Issue.2 , pp. 187-206
    • Clark, S.1    Weir, D.2
  • 8
    • 0041167390 scopus 로고    scopus 로고
    • Role of word sense disambiguation in lexical acquisition: Predicting semantics from syntactic cues
    • Copenhagen, Denmark
    • Bonnie J. Dorr and Doug Jones. 1996. Role of Word Sense Disambiguation in LexicalAcquisition: Predicting Semantics from Syntactic Cues. In Proceedings of the 16th International Conference on Computational Linguistics, pages 322-327, Copenhagen, Denmark.
    • (1996) Proceedings of the 16th International Conference on Computational Linguistics , pp. 322-327
    • Dorr, B.J.1    Jones, D.2
  • 10
    • 44949123685 scopus 로고    scopus 로고
    • A general feature space for automatic verb classification
    • To appear
    • Eric Joanis, Suzanne Stevenson, and David James. 2008? A General Feature Space for Automatic Verb Classification. Natural Language Engineering. To appear.
    • (2008) Natural Language Engineering
    • Joanis, E.1    Stevenson, S.2    James, D.3
  • 16
    • 0002532056 scopus 로고
    • The estimation of stochastic context-free grammars using the inside- outside algorithm
    • Karim Lari and Steve J. Young. 1990. The Estimation of Stochastic Context-Free Grammars using the Inside- Outside Algorithm. Computer Speech and Language, 4:35-56.
    • (1990) Computer Speech and Language , vol.4 , pp. 35-56
    • Lari, K.1    Young, S.J.2
  • 17
    • 0000216534 scopus 로고    scopus 로고
    • Generalizing case frames using a thesaurus and the MDL principle
    • Hang Li and Naoki Abe. 1998. Generalizing Case Frames Using a Thesaurus and the MDL Principle. Computational Linguistics, 24(2):217-244.
    • (1998) Computational Linguistics , vol.24 , Issue.2 , pp. 217-244
    • Li, H.1    Abe, N.2
  • 18
    • 0039190150 scopus 로고    scopus 로고
    • Automatic verb classification based on statistical distributions of argument structure
    • Paola Merlo and Suzanne Stevenson. 2001. Automatic Verb Classification Based on Statistical Distributions of Argument Structure. Computational Linguistics, 27(3):373-408.
    • (2001) Computational Linguistics , vol.27 , Issue.3 , pp. 373-408
    • Merlo, P.1    Stevenson, S.2
  • 22
    • 0018015137 scopus 로고
    • Modeling by shortest data description
    • Jorma Rissanen. 1978. Modeling by Shortest Data Description. Automatica, 14:465-471.
    • (1978) Automatica , vol.14 , pp. 465-471
    • Rissanen, J.1
  • 24
    • 33745853494 scopus 로고    scopus 로고
    • Experiments on the automatic induction of German semantic verb classes
    • Sabine Schulte imWalde. 2006. Experiments on the Automatic Induction of German Semantic Verb Classes. Computational Linguistics, 32(2):159-194.
    • (2006) Computational Linguistics , vol.32 , Issue.2 , pp. 159-194
    • Imwalde, S.S.1
  • 25
    • 0041112231 scopus 로고    scopus 로고
    • Learning methods to combine linguistic indicators: Improving aspectual classification and revealing linguistic insights
    • Eric V. Siegel and Kathleen R. McKeown. 2000. Learning Methods to Combine Linguistic Indicators: Improving Aspectual Classification and Revealing Linguistic Insights. Computational Linguistics, 26(4):595-628.
    • (2000) Computational Linguistics , vol.26 , Issue.4 , pp. 595-628
    • Siegel, E.V.1    McKeown, K.R.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.