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    A Unified Bayesian Model of Scripts, Frames and Language

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    https://www.google.com/url?q=https://www.aaai.org/ocs/index.php/AAAI/AAAI16/paper/download/12092/11994&sa=U&ved=0ahUKEwiU64CYlJDeAhVF11kKHZAwAs0QFggEMAA&client=internal-uds-cse&cx=016314354884912110518:gwmynp16xuu&usg=AOvVaw2a2VsXzYorHgfeeGvYILz_
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    http://hdl.handle.net/11603/11805
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    • UMBC Computer Science and Electrical Engineering Department
    • UMBC Faculty Collection
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    Author/Creator
    Ferraro, Francis
    Durme, Benjamin Van
    Date
    2016-02-12
    Type of Work
    7 pages
    Text
    conference papers and proceedings pre-print
    Citation of Original Publication
    Francis Ferraro and Benjamin Van Durme, A Unified Bayesian Model of Scripts, Frames and Language, Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, 2016.
    Rights
    This item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
    Subjects
    learning
    natural language processing
    semantics
    natural languge
    UMBC Ebiquity Research Group
    Abstract
    We present the first probabilistic model to capture all levels of the Minsky Frame structure, with the goal of corpus-based induction of scenario definitions. Our model unifies prior efforts in discourse-level modeling with that of Fill-more's related notion of frame, as captured in sentence-level, FrameNet semantic parses; as part of this, we resurrect the coupling among Minsky's frames, Schank's scripts and Fill-more's frames, as originally laid out by those authors. Empirically, our approach yields improved scenario representations, reflected quantitatively in lower surprisal and more coherent latent scenarios.


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    Albin O. Kuhn Library & Gallery
    University of Maryland, Baltimore County
    1000 Hilltop Circle
    Baltimore, MD 21250
    www.umbc.edu/scholarworks

    Contact information:
    Email: scholarworks-group@umbc.edu
    Phone: 410-455-3021


    If you wish to submit a copyright complaint or withdrawal request, please email mdsoar-help@umd.edu.