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    IPM
    2008
    159views more  IPM 2008»
    Exploring syntactic structured features over parse trees for relation extraction using kernel methods
    13 years 7 months ago
    Exploring syntactic structured features over parse trees for relation extraction using kernel methods
    Download  nlp.suda.edu.cn
    Extracting semantic relationships between entities from text documents is challenging in information extraction and important for deep information processing and management. This ...
    Min Zhang, Guodong Zhou, AiTi Aw
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    ACL
    2009
    166views Computational Linguistics» more  ACL 2009»
    A Novel Discourse Parser Based on Support Vector Machine Classification
    13 years 5 months ago
    A Novel Discourse Parser Based on Support Vector Machine Classification
    Download  www.aclweb.org
    This paper introduces a new algorithm to parse discourse within the framework of Rhetorical Structure Theory (RST). Our method is based on recent advances in the field of statisti...
    David duVerle, Helmut Prendinger
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    ICML
    2005
    IEEE
    147views Machine Learning» more  ICML 2005»
    Learning Gaussian processes from multiple tasks
    14 years 8 months ago
    Learning Gaussian processes from multiple tasks
    Download  wwwbrauer.in.tum.de
    We consider the problem of multi-task learning, that is, learning multiple related functions. Our approach is based on a hierarchical Bayesian framework, that exploits the equival...
    Kai Yu, Volker Tresp, Anton Schwaighofer
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    ACL
    2004
    185views Computational Linguistics» more  ACL 2004»
    Applying Machine Learning to Chinese Temporal Relation Resolution
    13 years 8 months ago
    Applying Machine Learning to Chinese Temporal Relation Resolution
    Download  acl.ldc.upenn.edu
    Temporal relation resolution involves extraction of temporal information explicitly or implicitly embedded in a language. This information is often inferred from a variety of inte...
    Wenjie Li, Kam-Fai Wong, Guihong Cao, Chunfa Yuan
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    NIPS
    2008
    138views Information Technology» more  NIPS 2008»
    Exploring Large Feature Spaces with Hierarchical Multiple Kernel Learning
    13 years 8 months ago
    Exploring Large Feature Spaces with Hierarchical Multiple Kernel Learning
    Download  eprints.pascal-network.org
    For supervised and unsupervised learning, positive definite kernels allow to use large and potentially infinite dimensional feature spaces with a computational cost that only depe...
    Francis Bach
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