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    NP animacy identification for anaphora resolution

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    Authors
    Orasan, Constantin
    Evans, Richard
    Issue Date
    2007
    
    Metadata
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    Abstract
    In anaphora resolution for English, animacy identification can play an integral role in the application of agreement restrictions between pronouns and candidates, and as a result, can improve the accuracy of anaphora resolution systems. In this paper, two methods for animacy identification are proposed and evaluated using intrinsic and extrinsic measures. The first method is a rule-based one which uses information about the unique beginners in WordNet to classify NPs on the basis of their animacy. The second method relies on a machine learning algorithm which exploits a WordNet enriched with animacy information for each sense. The effect of word sense disambiguation on the two methods is also assessed. The intrinsic evaluation reveals that the machine learning method reaches human levels of performance. The extrinsic evaluation demonstrates that animacy identification can be beneficial in anaphora resolution, especially in the cases where animate entities are identified with high precision.
    Citation
    Journal of Artificial Intelligence Research, 29 (2007): 79-103
    Publisher
    American Association for Artificial Intelligence
    Journal
    Journal of Artificial Intelligence Research
    URI
    http://hdl.handle.net/2436/27896
    DOI
    10.1613/jair.2179
    Additional Links
    https://www.jair.org/index.php/jair/article/view/10499
    Type
    Journal article
    Language
    en
    ISSN
    11076 - 9757
    ae974a485f413a2113503eed53cd6c53
    10.1613/jair.2179
    Scopus Count
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    Research Institute in Information and Language Processing

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