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    <titleInfo>
        <title>Using Language Learner Data for Metaphor Detection</title>
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    <name type="personal">
        <namePart type="given">Egon</namePart>
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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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        <name type="personal">
            <namePart type="given">Ekaterina</namePart>
            <namePart type="family">Shutova</namePart>
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        <name type="personal">
            <namePart type="given">Patricia</namePart>
            <namePart type="family">Lichtenstein</namePart>
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    <abstract>This article describes the system that participated in the shared task on metaphor detection on the Vrije University Amsterdam Metaphor Corpus (VUA). The ST was part of the workshop on processing figurative language at the 16th annual conference of the North American Chapter of the Association for Computational Linguistics (NAACL2018). The system combines a small assertion of trending techniques, which implement matured methods from NLP and ML; in particular, the system uses word embeddings from standard corpora and from corpora representing different proficiency levels of language learners in a LSTM BiRNN architecture. The system is available under the APLv2 open-source license.</abstract>
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%0 Conference Proceedings
%T Using Language Learner Data for Metaphor Detection
%A Stemle, Egon
%A Onysko, Alexander
%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 stemle-onysko-2018-using
%X This article describes the system that participated in the shared task on metaphor detection on the Vrije University Amsterdam Metaphor Corpus (VUA). The ST was part of the workshop on processing figurative language at the 16th annual conference of the North American Chapter of the Association for Computational Linguistics (NAACL2018). The system combines a small assertion of trending techniques, which implement matured methods from NLP and ML; in particular, the system uses word embeddings from standard corpora and from corpora representing different proficiency levels of language learners in a LSTM BiRNN architecture. The system is available under the APLv2 open-source license.
%R 10.18653/v1/W18-0918
%U https://aclanthology.org/W18-0918/
%U https://doi.org/10.18653/v1/W18-0918
%P 133-138

Markdown (Informal)

[Using Language Learner Data for Metaphor Detection](https://aclanthology.org/W18-0918/) (Stemle & Onysko, Fig-Lang 2018)

• Using Language Learner Data for Metaphor Detection (Stemle & Onysko, Fig-Lang 2018)

ACL

• Egon Stemle and Alexander Onysko. 2018. Using Language Learner Data for Metaphor Detection. In Proceedings of the Workshop on Figurative Language Processing, pages 133–138, New Orleans, Louisiana. Association for Computational Linguistics.