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    ACL
    2006
    193views Computational Linguistics» more  ACL 2006»
    A Phrase-Based Statistical Model for SMS Text Normalization
    13 years 10 months ago
    A Phrase-Based Statistical Model for SMS Text Normalization
    Download  acl.ldc.upenn.edu
    Short Messaging Service (SMS) texts behave quite differently from normal written texts and have some very special phenomena. To translate SMS texts, traditional approaches model s...
    AiTi Aw, Min Zhang, Juan Xiao, Jian Su
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    ECTEL
    2007
    Springer
    129views Machine Learning» more  ECTEL 2007»
    FAsTA: A Folksonomy-Based Automatic Metadata Generator
    14 years 2 months ago
    FAsTA: A Folksonomy-Based Automatic Metadata Generator
    Download  eprints.ecs.soton.ac.uk
    Folksonomies provide a free source of keywords describing web resources, however, these keywords are free form and unstructured. In this paper, we describe a novel tool that conver...
    Hend S. Al-Khalifa, Hugh C. Davis
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    EURODAC
    1995
    IEEE
    126views VHDL» more  EURODAC 1995»
    Use of embedded scheduling to compile VHDL for effective parallel simulation
    14 years 9 days ago
    Use of embedded scheduling to compile VHDL for effective parallel simulation
    Download  www.cs.york.ac.uk
    This paper describes VHDL compilation techniques, embodied in the Auriga compiler [3,14], which facilitate parallel or distributed simulation by embedding evaluation scheduling in...
    John Willis, Zhiyuan Li, Tsang-Puu Lin
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    LISP
    2006
    105views more  LISP 2006»
    Call-by-push-value: Decomposing call-by-value and call-by-name
    13 years 8 months ago
    Call-by-push-value: Decomposing call-by-value and call-by-name
    Download  www.cs.bham.ac.uk
    We present the call-by-push-value (CBPV) calculus, which decomposes the typed call-by-value (CBV) and typed call-by-name (CBN) paradigms into fine-grain primitives. On the operatio...
    Paul Blain Levy
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    ACL
    2006
    161views Computational Linguistics» more  ACL 2006»
    Minimum Risk Annealing for Training Log-Linear Models
    13 years 10 months ago
    Minimum Risk Annealing for Training Log-Linear Models
    Download  www.cs.jhu.edu
    When training the parameters for a natural language system, one would prefer to minimize 1-best loss (error) on an evaluation set. Since the error surface for many natural languag...
    David A. Smith, Jason Eisner
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