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    <titleInfo>
        <title>Data-Driven Metaphor Recognition and Explanation</title>
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    <name type="personal">
        <namePart type="given">Hongsong</namePart>
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        <namePart type="given">Q</namePart>
        <namePart type="family">Zhu</namePart>
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    <name type="personal">
        <namePart type="given">Haixun</namePart>
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    <abstract>Recognizing metaphors and identifying the source-target mappings is an important task as metaphorical text poses a big challenge for machine reading. To address this problem, we automatically acquire a metaphor knowledge base and an isA knowledge base from billions of web pages. Using the knowledge bases, we develop an inference mechanism to recognize and explain the metaphors in the text. To our knowledge, this is the first purely data-driven approach of probabilistic metaphor acquisition, recognition, and explanation. Our results shows that it significantly outperforms other state-of-the-art methods in recognizing and explaining metaphors.</abstract>
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    <identifier type="doi">10.1162/tacl_a_00235</identifier>
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        <url>https://aclanthology.org/Q13-1031</url>
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        <date>2013</date>
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%0 Journal Article
%T Data-Driven Metaphor Recognition and Explanation
%A Li, Hongsong
%A Zhu, Kenny Q.
%A Wang, Haixun
%J Transactions of the Association for Computational Linguistics
%D 2013
%V 1
%I MIT Press
%C Cambridge, MA
%F li-etal-2013-data
%X Recognizing metaphors and identifying the source-target mappings is an important task as metaphorical text poses a big challenge for machine reading. To address this problem, we automatically acquire a metaphor knowledge base and an isA knowledge base from billions of web pages. Using the knowledge bases, we develop an inference mechanism to recognize and explain the metaphors in the text. To our knowledge, this is the first purely data-driven approach of probabilistic metaphor acquisition, recognition, and explanation. Our results shows that it significantly outperforms other state-of-the-art methods in recognizing and explaining metaphors.
%R 10.1162/tacl_a_00235
%U https://aclanthology.org/Q13-1031
%U https://doi.org/10.1162/tacl_a_00235
%P 379-390

Markdown (Informal)

[Data-Driven Metaphor Recognition and Explanation](https://aclanthology.org/Q13-1031) (Li et al., TACL 2013)

• Data-Driven Metaphor Recognition and Explanation (Li et al., TACL 2013)

ACL

• Hongsong Li, Kenny Q. Zhu, and Haixun Wang. 2013. Data-Driven Metaphor Recognition and Explanation. Transactions of the Association for Computational Linguistics, 1:379–390.