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    ACL
    2009
    108views Computational Linguistics» more  ACL 2009»
    Joint Decoding with Multiple Translation Models
    13 years 9 months ago
    Joint Decoding with Multiple Translation Models
    Download  mtgroup.ict.ac.cn
    Current SMT systems usually decode with single translation models and cannot benefit from the strengths of other models in decoding phase. We instead propose joint decoding, a met...
    Yang Liu, Haitao Mi, Yang Feng, Qun Liu
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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 9 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
    112views Computational Linguistics» more  ACL 2009»
    Minimized Models for Unsupervised Part-of-Speech Tagging
    13 years 9 months ago
    Minimized Models for Unsupervised Part-of-Speech Tagging
    Download  www.isi.edu
    We describe a novel method for the task of unsupervised POS tagging with a dictionary, one that uses integer programming to explicitly search for the smallest model that explains ...
    Sujith Ravi, Kevin Knight
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    ACL
    2009
    180views Computational Linguistics» more  ACL 2009»
    A Graph-based Semi-Supervised Learning for Question-Answering
    13 years 9 months ago
    A Graph-based Semi-Supervised Learning for Question-Answering
    Download  www.aclweb.org
    We present a graph-based semi-supervised learning for the question-answering (QA) task for ranking candidate sentences. Using textual entailment analysis, we obtain entailment sco...
    Asli Çelikyilmaz, Marcus Thint, Zhiheng Hua...
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    ACL
    2009
    83views Computational Linguistics» more  ACL 2009»
    Distributional Representations for Handling Sparsity in Supervised Sequence-Labeling
    13 years 9 months ago
    Distributional Representations for Handling Sparsity in Supervised Sequence-Labeling
    Download  www.aclweb.org
    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
    166views Computational Linguistics» more  ACL 2009»
    A Novel Discourse Parser Based on Support Vector Machine Classification
    13 years 9 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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    ACL
    2009
    99views Computational Linguistics» more  ACL 2009»
    Jointly Identifying Temporal Relations with Markov Logic
    13 years 9 months ago
    Jointly Identifying Temporal Relations with Markov Logic
    Download  cl.naist.jp
    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
    105views Computational Linguistics» more  ACL 2009»
    Semi-supervised Learning of Dependency Parsers using Generalized Expectation Criteria
    13 years 9 months ago
    Semi-supervised Learning of Dependency Parsers using Generalized Expectation Criteria
    Download  www.cs.umass.edu
    In this paper, we propose a novel method for semi-supervised learning of nonprojective log-linear dependency parsers using directly expressed linguistic prior knowledge (e.g. a no...
    Gregory Druck, Gideon S. Mann, Andrew McCallum
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    ACL
    2009
    175views Computational Linguistics» more  ACL 2009»
    Who, What, When, Where, Why? Comparing Multiple Approaches to the Cross-Lingual 5W Task
    13 years 9 months ago
    Who, What, When, Where, Why? Comparing Multiple Approaches to the Cross-Lingual 5W Task
    Download  nlp.cs.qc.cuny.edu
    Cross-lingual tasks are especially difficult due to the compounding effect of errors in language processing and errors in machine translation (MT). In this paper, we present an er...
    Kristen Parton, Kathleen McKeown, Bob Coyne, Mona ...
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    ACL
    2009
    93views Computational Linguistics» more  ACL 2009»
    Learning with Annotation Noise
    13 years 9 months ago
    Learning with Annotation Noise
    Download  www.aclweb.org
    It is usually assumed that the kind of noise existing in annotated data is random classification noise. Yet there is evidence that differences between annotators are not always ra...
    Eyal Beigman, Beata Beigman Klebanov
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