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    AUTOMATIC DISAMBIGUATION OF CHINESE MODAL EXPRESSIONS - A SUPERVISED MACHINE LEARNING EXPERIMENT

    Cover for AUTOMATIC DISAMBIGUATION OF CHINESE MODAL EXPRESSIONS - A SUPERVISED MACHINE LEARNING EXPERIMENT
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    View/Open: Chi_georgetown_0076M_12309.pdf (1.8MB) Bookview

    Creator
    Chi, Ting
    Advisor
    Davis, Anthony R.
    Portner, Paul H.
    Abstract
    This thesis reports an annotation on Chinese modal expressions in Chinese Treebank (CHTB) 4.0, with eleven attributes that may affect the reading of modal expressions. The annotated data provide distributional information about modality types and attributes of Chinese modal expressions, signaling terms that determine modality types, and training data for the modality type disambiguation. With the annotated data, this thesis presents a supervised machine learning experiment on the modality type disambiguation of Chinese modal expressions. This disambiguation is based on Priority and Non-Priority classification, using three algorithms: Naive Bayes, maximum entropy, and decision tree, and features extracted from surrounding words as well as annotated data. The results show that maximum entropy has the best performance among the three algorithms. In addition, among the features that are used to train the classifiers, features extracted from annotated data achieve the highest accuracy in predicting the modality type of Chinese modal expressions, which is 0.9383.
    Description
    M.S.
    Permanent Link
    http://hdl.handle.net/10822/558392
    Date Published
    2013
    Subject
    Chinese; Disambiguation; Modality; Linguistics; Information technology; Computer science; Linguistics; Information technology; Computer science;
    Type
    thesis
    Publisher
    Georgetown University
    Extent
    88 leaves
    Collections
    • Graduate Theses and Dissertations - Linguistics
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    Georgetown University Seal
    ©2009 - 2022 Georgetown University Library
    37th & O Streets NW
    Washington DC 20057-1174
    202.687.7385
    digitalscholarship@georgetown.edu
    Accessibility