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        <title>Computationally Constructed Concepts: A Machine Learning Approach to Metaphor Interpretation Using Usage-Based Construction Grammatical Cues</title>
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            <title>Proceedings of the Workshop on Figurative Language Processing</title>
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        <name type="personal">
            <namePart type="given">Beata</namePart>
            <namePart type="family">Beigman Klebanov</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Ekaterina</namePart>
            <namePart type="family">Shutova</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Patricia</namePart>
            <namePart type="family">Lichtenstein</namePart>
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            <namePart type="given">Smaranda</namePart>
            <namePart type="family">Muresan</namePart>
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    <abstract>The current study seeks to implement a deep learning classification algorithm using argument-structure level representation of metaphoric constructions, for the identification of source domain mappings in metaphoric utterances. It thus builds on previous work in computational metaphor interpretation (Mohler et al. 2014; Shutova 2010; Bollegala &amp; Shutova 2013; Hong 2016; Su et al. 2017) while implementing a theoretical framework based off of work in the interface of metaphor and construction grammar (Sullivan 2006, 2007, 2013). The results indicate that it is possible to achieve an accuracy of approximately 80.4% using the proposed method, combining construction grammatical features with a simple deep learning NN. I attribute this increase in accuracy to the use of constructional cues, extracted from the raw text of metaphoric instances.</abstract>
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%0 Conference Proceedings
%T Computationally Constructed Concepts: A Machine Learning Approach to Metaphor Interpretation Using Usage-Based Construction Grammatical Cues
%A Rosen, Zachary
%Y Beigman Klebanov, Beata
%Y Shutova, Ekaterina
%Y Lichtenstein, Patricia
%Y Muresan, Smaranda
%Y Wee, Chee
%S Proceedings of the Workshop on Figurative Language Processing
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana
%F rosen-2018-computationally
%X The current study seeks to implement a deep learning classification algorithm using argument-structure level representation of metaphoric constructions, for the identification of source domain mappings in metaphoric utterances. It thus builds on previous work in computational metaphor interpretation (Mohler et al. 2014; Shutova 2010; Bollegala & Shutova 2013; Hong 2016; Su et al. 2017) while implementing a theoretical framework based off of work in the interface of metaphor and construction grammar (Sullivan 2006, 2007, 2013). The results indicate that it is possible to achieve an accuracy of approximately 80.4% using the proposed method, combining construction grammatical features with a simple deep learning NN. I attribute this increase in accuracy to the use of constructional cues, extracted from the raw text of metaphoric instances.
%R 10.18653/v1/W18-0912
%U https://aclanthology.org/W18-0912/
%U https://doi.org/10.18653/v1/W18-0912
%P 102-109

Markdown (Informal)

[Computationally Constructed Concepts: A Machine Learning Approach to Metaphor Interpretation Using Usage-Based Construction Grammatical Cues](https://aclanthology.org/W18-0912/) (Rosen, Fig-Lang 2018)

• Computationally Constructed Concepts: A Machine Learning Approach to Metaphor Interpretation Using Usage-Based Construction Grammatical Cues (Rosen, Fig-Lang 2018)

ACL

• Zachary Rosen. 2018. Computationally Constructed Concepts: A Machine Learning Approach to Metaphor Interpretation Using Usage-Based Construction Grammatical Cues. In Proceedings of the Workshop on Figurative Language Processing, pages 102–109, New Orleans, Louisiana. Association for Computational Linguistics.