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    NIPS
    2007
    199views Information Technology» more  NIPS 2007»
    Discriminative Log-Linear Grammars with Latent Variables
    13 years 11 months ago
    Discriminative Log-Linear Grammars with Latent Variables
    Download  books.nips.cc
    We demonstrate that log-linear grammars with latent variables can be practically trained using discriminative methods. Central to efficient discriminative training is a hierarchi...
    Slav Petrov, Dan Klein
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    ACL
    1993
    96views Computational Linguistics» more  ACL 1993»
    Distributional Clustering of English Words
    13 years 11 months ago
    Distributional Clustering of English Words
    Download  acl.ldc.upenn.edu
    We describe and evaluate experimentally a method for clustering words according to their distribution in particular syntactic contexts. Words are represented by the relative frequ...
    Fernando C. N. Pereira, Naftali Tishby, Lillian Le...
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    ECCV
    2008
    Springer
    201views Computer Vision» more  ECCV 2008»
    Unsupervised Structure Learning: Hierarchical Recursive Composition, Suspicious Coincidence and Competitive Exclusion
    14 years 11 months ago
    Unsupervised Structure Learning: Hierarchical Recursive Composition, Suspicious Coincidence and Competitive Exclusion
    Download  people.csail.mit.edu
    Abstract. We describe a new method for unsupervised structure learning of a hierarchical compositional model (HCM) for deformable objects. The learning is unsupervised in the sense...
    Long Zhu, Chenxi Lin, Haoda Huang, Yuanhao Chen, A...
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    MT
    2006
    116views more  MT 2006»
    Dependency treelet translation: the convergence of statistical and example-based machine-translation?
    13 years 9 months ago
    Dependency treelet translation: the convergence of statistical and example-based machine-translation?
    Download  www.mt-archive.info
    We describe a novel approach to machine translation that combines the strengths of the two leading corpus-based approaches: Phrasal SMT and EBMT. We use a syntactically informed d...
    Christopher Quirk, Arul Menezes
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    ICCV
    2005
    IEEE
    248views Computer Vision» more  ICCV 2005»
    Learning Non-Generative Grammatical Models for Document Analysis
    14 years 3 months ago
    Learning Non-Generative Grammatical Models for Document Analysis
    Download  research.microsoft.com
    — We present a general approach for the hierarchical segmentation and labeling of document layout structures. This approach models document layout as a grammar and performs a glo...
    Michael Shilman, Percy Liang, Paul A. Viola
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