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  1.  8
    Exploring What Is Encoded in Distributional Word Vectors: A Neurobiologically Motivated Analysis.Akira Utsumi - 2020 - Cognitive Science 44 (6):e12844.
    The pervasive use of distributional semantic models or word embeddings for both cognitive modeling and practical application is because of their remarkable ability to represent the meanings of words. However, relatively little effort has been made to explore what types of information are encoded in distributional word vectors. Knowing the internal knowledge embedded in word vectors is important for cognitive modeling using distributional semantic models. Therefore, in this paper, we attempt to identify the knowledge encoded in word vectors by conducting (...)
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    Interpretive Diversity Explains Metaphor–Simile Distinction.Akira Utsumi - 2007 - Metaphor and Symbol 22 (4):291-312.
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    Computational Exploration of Metaphor Comprehension Processes Using a Semantic Space Model.Akira Utsumi - 2011 - Cognitive Science 35 (2):251-296.
    Recent metaphor research has revealed that metaphor comprehension involves both categorization and comparison processes. This finding has triggered the following central question: Which property determines the choice between these two processes for metaphor comprehension? Three competing views have been proposed to answer this question: the conventionality view (Bowdle & Gentner, 2005), aptness view (Glucksberg & Haught, 2006b), and interpretive diversity view (Utsumi, 2007); these views, respectively, argue that vehicle conventionality, metaphor aptness, and interpretive diversity determine the choice between the categorization (...)
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