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    ACL
    2009
    99views Computational Linguistics» more  ACL 2009»
    Jointly Identifying Temporal Relations with Markov Logic
    13 years 8 months ago
    Jointly Identifying Temporal Relations with Markov Logic
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    Recent work on temporal relation identification has focused on three types of relations between events: temporal relations between an event and a time expression, between a pair o...
    Katsumasa Yoshikawa, Sebastian Riedel, Masayuki As...
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    ACL
    2009
    166views Computational Linguistics» more  ACL 2009»
    A Novel Discourse Parser Based on Support Vector Machine Classification
    13 years 8 months ago
    A Novel Discourse Parser Based on Support Vector Machine Classification
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    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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    ACL
    2009
    83views Computational Linguistics» more  ACL 2009»
    Distributional Representations for Handling Sparsity in Supervised Sequence-Labeling
    13 years 8 months ago
    Distributional Representations for Handling Sparsity in Supervised Sequence-Labeling
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    Supervised sequence-labeling systems in natural language processing often suffer from data sparsity because they use word types as features in their prediction tasks. Consequently...
    Fei Huang, Alexander Yates
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    ACL
    2009
    94views Computational Linguistics» more  ACL 2009»
    A Ranking Approach to Stress Prediction for Letter-to-Phoneme Conversion
    13 years 8 months ago
    A Ranking Approach to Stress Prediction for Letter-to-Phoneme Conversion
    Download  www.aclweb.org
    Correct stress placement is important in text-to-speech systems, in terms of both the overall accuracy and the naturalness of pronunciation. In this paper, we formulate stress ass...
    Qing Dou, Shane Bergsma, Sittichai Jiampojamarn, G...
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    ACL
    2009
    121views Computational Linguistics» more  ACL 2009»
    Efficient Minimum Error Rate Training and Minimum Bayes-Risk Decoding for Translation Hypergraphs and Lattices
    13 years 8 months ago
    Efficient Minimum Error Rate Training and Minimum Bayes-Risk Decoding for Translation Hypergraphs and Lattices
    Download  aclweb.org
    Minimum Error Rate Training (MERT) and Minimum Bayes-Risk (MBR) decoding are used in most current state-of-theart Statistical Machine Translation (SMT) systems. The algorithms wer...
    Shankar Kumar, Wolfgang Macherey, Chris Dyer, Fran...
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