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    Extracting and modeling typical durations of events and habits from Twitter

    Cover for Extracting and modeling typical durations of events and habits from Twitter
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    Creator
    Williams, Jennifer Alexandra
    Advisor
    Katz, Graham
    Abstract
    This thesis presents recent work on a new method to automatically extract fine-grained duration information for common verbs using a large corpus of Twitter tweets. I present the results of distinguishing between habitual and episodic uses of verbs using semi-supervised machine learning methods. This work has resulted in a lexicon that contains verb lemmas and their associated episodic and habitual durations.
    Description
    M.S.
    Permanent Link
    http://hdl.handle.net/10822/557710
    Date Published
    2012
    Subject
    duration; habituality; information extraction; NLP; semantics; temporal reasoning; Linguistics; Computer science; Language and culture; Linguistics; Computer science; Language;
    Type
    thesis
    Publisher
    Georgetown University
    Extent
    82 leaves
    Collections
    • Graduate Theses and Dissertations - Linguistics
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    Georgetown University Seal
    ©2009 - 2023 Georgetown University Library
    37th & O Streets NW
    Washington DC 20057-1174
    202.687.7385
    digitalscholarship@georgetown.edu
    Accessibility