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    » The University of Michigan in Novelty 2004
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    TREC
    2004
    100views Information Technology» more  TREC 2004»
    The University of Michigan in Novelty 2004
    14 years 11 months ago
    The University of Michigan in Novelty 2004
    Download  trec.nist.gov
    This year we participated in the Novelty track. To find the relevant sentences, we combine sentence salience features that are inherited from text summarization domain with other ...
    Günes Erkan
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    TREC
    2004
    128views Information Technology» more  TREC 2004»
    Columbia University in the Novelty Track at TREC 2004
    14 years 11 months ago
    Columbia University in the Novelty Track at TREC 2004
    Download  trec.nist.gov
    Our system for the Novelty Track at TREC 2004 looks beyond sentence boundaries as well as within sentences to identify novel, nonduplicative passages. It tries to identify text sp...
    Barry Schiffman, Kathleen McKeown
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    TREC
    2004
    147views Information Technology» more  TREC 2004»
    Experiments in Terabyte Searching, Genomic Retrieval and Novelty Detection for TREC 2004
    14 years 11 months ago
    Experiments in Terabyte Searching, Genomic Retrieval and Novelty Detection for TREC 2004
    Download  trec.nist.gov
    : In TREC2004, Dublin City University took part in three tracks, Terabyte (in collaboration with University College Dublin), Genomic and Novelty. In this paper we will discuss each...
    Stephen Blott, Fabrice Camous, Paul Ferguson, Geor...
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    TREC
    2004
    101views Information Technology» more  TREC 2004»
    Novelty, Question Answering and Genomics: The University of Iowa Response
    14 years 11 months ago
    Novelty, Question Answering and Genomics: The University of Iowa Response
    Download  trec.nist.gov
    David Eichmann, Yi Zhang 0006, Shannon Bradshaw, X...
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    ICML
    2004
    IEEE
    161views Machine Learning» more  ICML 2004»
    Using relative novelty to identify useful temporal abstractions in reinforcement learning
    15 years 10 months ago
    Using relative novelty to identify useful temporal abstractions in reinforcement learning
    Download  www.cs.umass.edu
    lative Novelty to Identify Useful Temporal Abstractions in Reinforcement Learning ?Ozg?ur S?im?sek ozgur@cs.umass.edu Andrew G. Barto barto@cs.umass.edu Department of Computer Scie...
    Özgür Simsek, Andrew G. Barto
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