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  1.  3
    Interpretive Diversity Explains Metaphor–Simile Distinction.Akira Utsumi - 2007 - Metaphor and Symbol 22 (4):291-312.
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    Exploring What Is Encoded in Distributional Word Vectors: A Neurobiologically Motivated Analysis.Akira Utsumi - 2020 - Cognitive Science 44 (6).
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  3.  36
    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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    A Method for Extracting Important Segments From Documents Using Support Vector Machines: Toward Automatic Text SummarizationSupport Vector Machineを用いた文書の重要文節抽出―要約文生成に向けて―.Daisuke Suzuki & Akira Utsumi - 2006 - Transactions of the Japanese Society for Artificial Intelligence 21:330-339.
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    Music Retrieval Based on the Relation Between Color Association and Lyrics.Tetsuaki Nakamur, Akira Utsumi & Maki Sakamoto - 2012 - Transactions of the Japanese Society for Artificial Intelligence 27 (3):163-175.
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    Extracting Causal Knowledge by Time Series Analysis of Events.Hiroki Ono & Akira Utsumi - 2015 - Transactions of the Japanese Society for Artificial Intelligence 30 (1):12-21.
    Causal knowledge is important for decision-making and risk aversion. However, it takes much time and effort to extract causal knowledge manually from a large-scale corpus. Therefore, many studies have proposed several methods for automatically extracting causal knowledge. These methods use a variety of linguistic or textual cues indicating causality on the basis of the assumption that causally related events tend to co-occur within a document. However, because of this assumption, they cannot extract causal knowledge that is not explicitly described in (...)
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