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    Measuring Metaphoricity (2014)

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    Conference Contributions - Published
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    http://hdl.handle.net/10092/17136
    
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    • Arts: Conference Contributions [218]
    Authors
    Dunn J
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    Abstract

    This paper presents the first computationally-derived scalar measurement of metaphoricity. Each input sentence is given a value between 0 and 1 which represents how metaphoric that sentence is. This measure achieves a correlation of 0.450 (Pearson’s R, p <0.01) with an experimental measure of metaphoricity involving human participants. While far from perfect, this scalar measure of metaphoricity allows different thresholds for metaphoricity so that metaphor identification can be fitted for specific tasks and datasets. When reduced to a binary classification evaluation using the VU Amsterdam Metaphor Corpus, the system achieves an F-Measure of 0.608, slightly lower than the comparable binary classification system’s 0.638 and competitive with existing approaches.

    Citation
    Dunn J (2014). Measuring Metaphoricity. Association for Computational Linguistics (ACL).
    This citation is automatically generated and may be unreliable. Use as a guide only.
    ANZSRC Fields of Research
    20 - Language, Communication and Culture::2004 - Linguistics::200402 - Computational Linguistics
    47 - Language, communication and culture::4704 - Linguistics::470409 - Linguistic structures (incl. phonology, morphology and syntax)
    17 - Psychology and Cognitive Sciences::1702 - Cognitive Science::170204 - Linguistic Processes (incl. Speech Production and Comprehension)
    Rights
    Creative Commons Attribution-NonCommercial-ShareAlike 3.0 International License

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      This paper evaluates four metaphor identification systems on the 200,000 word VU Amsterdam Metaphor Corpus, comparing results by genre and by sub-class of metaphor. The paper then compares the rate of agreement between ...
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