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    EACL
    2006
    ACL Anthology
    129views Natural Language Processing» more  EACL 2006»
    Discriminative Sentence Compression with Soft Syntactic Evidence
    14 years 11 months ago
    Discriminative Sentence Compression with Soft Syntactic Evidence
    Download  acl.ldc.upenn.edu
    We present a model for sentence compression that uses a discriminative largemargin learning framework coupled with a novel feature set defined on compressed bigrams as well as dee...
    Ryan T. McDonald
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    EACL
    2006
    ACL Anthology
    120views Natural Language Processing» more  EACL 2006»
    Online Learning of Approximate Dependency Parsing Algorithms
    14 years 11 months ago
    Online Learning of Approximate Dependency Parsing Algorithms
    Download  www.seas.upenn.edu
    In this paper we extend the maximum spanning tree (MST) dependency parsing framework of McDonald et al. (2005c) to incorporate higher-order feature representations and allow depen...
    Ryan T. McDonald, Fernando C. N. Pereira
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    EACL
    2006
    ACL Anthology
    131views Natural Language Processing» more  EACL 2006»
    Making Tree Kernels Practical for Natural Language Learning
    14 years 11 months ago
    Making Tree Kernels Practical for Natural Language Learning
    Download  acl.ldc.upenn.edu
    In recent years tree kernels have been proposed for the automatic learning of natural language applications. Unfortunately, they show (a) an inherent super linear complexity and (...
    Alessandro Moschitti
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    EACL
    2006
    ACL Anthology
    84views Natural Language Processing» more  EACL 2006»
    Example-Based Metonymy Recognition for Proper Nouns
    14 years 11 months ago
    Example-Based Metonymy Recognition for Proper Nouns
    Download  acl.ldc.upenn.edu
    Metonymy recognition is generally approached with complex algorithms that rely heavily on the manual annotation of training and test data. This paper will relieve this complexity ...
    Yves Peirsman
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    EACL
    2006
    ACL Anthology
    91views Natural Language Processing» more  EACL 2006»
    A Probabilistic Answer Type Model
    14 years 11 months ago
    A Probabilistic Answer Type Model
    Download  acl.ldc.upenn.edu
    All questions are implicitly associated with an expected answer type. Unlike previous approaches that require a predefined set of question types, we present a method for dynamical...
    Christopher Pinchak, Dekang Lin
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