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    ACL
    2010
    149views Computational Linguistics» more  ACL 2010»
    Hierarchical Joint Learning: Improving Joint Parsing and Named Entity Recognition with Non-Jointly Labeled Data
    13 years 6 months ago
    Hierarchical Joint Learning: Improving Joint Parsing and Named Entity Recognition with Non-Jointly Labeled Data
    Download  www-nlp.stanford.edu
    One of the main obstacles to producing high quality joint models is the lack of jointly annotated data. Joint modeling of multiple natural language processing tasks outperforms si...
    Jenny Rose Finkel, Christopher D. Manning
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    INTERSPEECH
    2010
    140views Signal Processing» more  INTERSPEECH 2010»
    Using dependency parsing and machine learning for factoid question answering on spoken documents
    13 years 3 months ago
    Using dependency parsing and machine learning for factoid question answering on spoken documents
    Download  www.lsi.upc.edu
    This paper presents our experiments in question answering for speech corpora. These experiments focus on improving the answer extraction step of the QA process. We present two app...
    Pere Comas, Jordi Turmo, Lluís Màrqu...
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    CVPR
    2008
    IEEE
    168views Computer Vision» more  CVPR 2008»
    Max Margin AND/OR Graph learning for parsing the human body
    14 years 11 months ago
    Max Margin AND/OR Graph learning for parsing the human body
    Download  www.stat.ucla.edu
    We present a novel structure learning method, Max Margin AND/OR Graph (MM-AOG), for parsing the human body into parts and recovering their poses. Our method represents the human b...
    Long Zhu, Yuanhao Chen, Yifei Lu, Chenxi Lin, Alan...
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    EMNLP
    2007
    91views Natural Language Processing» more  EMNLP 2007»
    Structural Correspondence Learning for Dependency Parsing
    13 years 10 months ago
    Structural Correspondence Learning for Dependency Parsing
    Download  www.r.dl.itc.u-tokyo.ac.jp
    Following (Blitzer et al., 2006), we present an application of structural correspondence learning to non-projective dependency parsing (McDonald et al., 2005). To induce the corre...
    Nobuyuki Shimizu, Hiroshi Nakagawa
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    ACL
    2006
    112views Computational Linguistics» more  ACL 2006»
    Trimming CFG Parse Trees for Sentence Compression Using Machine Learning Approaches
    13 years 10 months ago
    Trimming CFG Parse Trees for Sentence Compression Using Machine Learning Approaches
    Download  www-tsujii.is.s.u-tokyo.ac.jp
    Sentence compression is a task of creating a short grammatical sentence by removing extraneous words or phrases from an original sentence while preserving its meaning. Existing me...
    Yuya Unno, Takashi Ninomiya, Yusuke Miyao, Jun-ich...
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