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Volumn , Issue , 2008, Pages 41-48

Using LDA to detect semantically incoherent documents

Author keywords

[No Author keywords available]

Indexed keywords

CONTENT DETECTION; DIRICHLET; DOCUMENT SETS; TEXT SEGMENTATION; TOPIC DETECTION; TOPIC DISTRIBUTIONS;

EID: 84865096908     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.3115/1596324.1596332     Document Type: Conference Paper
Times cited : (39)

References (21)
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    • Latent dirichlet allocation
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    • Blei, David M., Andrew Y. Ng, and Michael I. Jordan. 2002. Latent Dirichlet allocation. In Dietterich, Thomas G., Suzanna Becker, and Zoubin Ghahramani, editors, Advances in Neural Information Processing Systems (NIPS), volume 14, pages 601-608, Cambridge, MA. MIT Press.
    • (2002) Advances in Neural Information Processing Systems (NIPS) , vol.14 , pp. 601-608
    • Blei, D.M.1    Ng, A.Y.2    Jordan, M.I.3
  • 6
    • 84937189426 scopus 로고    scopus 로고
    • The measurement of textual coherence with Latent Semantic Analysis
    • Foltz, P.W., W. Kintsch, and T.K. Landauer. 1998. The measurement of textual coherence with Latent Semantic Analysis. Discourse Processes, 25(2-3):285-307.
    • (1998) Discourse Processes , vol.25 , Issue.2-3 , pp. 285-307
    • Foltz, P.W.1    Kintsch, W.2    Landauer, T.K.3
  • 11
    • 0001819680 scopus 로고    scopus 로고
    • TextTiling: Segmenting texts into multi-paragraph subtopic passages
    • Hearst, Marti. 1997. TextTiling: Segmenting texts into multi-paragraph subtopic passages. Computational Linguistics, 23(1):33-64.
    • (1997) Computational Linguistics , vol.23 , Issue.1 , pp. 33-64
    • Hearst, M.1
  • 13
    • 0034818212 scopus 로고    scopus 로고
    • Unsupervised learning by probabilistic latent semantic analysis
    • Hofmann, Thomas. 2001. Unsupervised learning by probabilistic latent semantic analysis. Machine Learning Journal, 42(1):177-196.
    • (2001) Machine Learning Journal , vol.42 , Issue.1 , pp. 177-196
    • Hofmann, T.1
  • 15
    • 0008815681 scopus 로고    scopus 로고
    • Exponentiated gradient versus gradient descent for linear predictors
    • Kivinen, Jyrki and Manfrud K. Warmuth. 1997. Exponentiated gradient versus gradient descent for linear predictors. Information and Computation, 132:1-63.
    • (1997) Information and Computation , vol.132 , pp. 1-63
    • Kivinen, J.1    Warmuth, M.K.2
  • 16
    • 84876811202 scopus 로고    scopus 로고
    • RCV1: A new benchmark collection for text categorization research
    • Lewis, David D., Yiming Yang, Tony Rose, and Fan Li. 2004. RCV1: A new benchmark collection for text categorization research. Machine Learning Research, 5:361-397.
    • (2004) Machine Learning Research , vol.5 , pp. 361-397
    • Lewis, D.D.1    Yang, Y.2    Rose, T.3    Li, F.4
  • 18
    • 0033886806 scopus 로고    scopus 로고
    • Text classification from labeled and unlabeled documents using EM
    • Nigam, K., A. K. McCallum, S. Thrun, and T. M. Mitchell. 2000. Text classification from labeled and unlabeled documents using EM. Machine Learning, 39(2/3):103-134.
    • (2000) Machine Learning , vol.39 , Issue.2-3 , pp. 103-134
    • Nigam, K.1    McCallum, A.K.2    Thrun, S.3    Mitchell, T.M.4
  • 20
    • 34247334686 scopus 로고    scopus 로고
    • Inference and evaluation of the multinomial mixture model for text clustering
    • September
    • Rigouste, Loïs, Olivier Cappé, and François Yvon. 2007. Inference and evaluation of the multinomial mixture model for text clustering. Information Processing and Management, 43(5):1260-1280, September.
    • (2007) Information Processing and Management , vol.43 , Issue.5 , pp. 1260-1280
    • Rigouste, L.1    Cappé, O.2    Yvon, F.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.