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        <title>An LSTM-CRF Based Approach to Token-Level Metaphor Detection</title>
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    <name type="personal">
        <namePart type="given">Malay</namePart>
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    <name type="personal">
        <namePart type="given">Ashim</namePart>
        <namePart type="family">Gupta</namePart>
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            <title>Proceedings of the Workshop on Figurative Language Processing</title>
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            <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>
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    <abstract>Automatic processing of figurative languages is gaining popularity in NLP community for their ubiquitous nature and increasing volume. In this era of web 2.0, automatic analysis of sarcasm and metaphors is important for their extensive usage. Metaphors are a part of figurative language that compares different concepts, often on a cognitive level. Many approaches have been proposed for automatic detection of metaphors, even using sequential models or neural networks. In this paper, we propose a method for detection of metaphors at the token level using a hybrid model of Bidirectional-LSTM and CRF. We used fewer features, as compared to the previous state-of-the-art sequential model. On experimentation with VUAMC, our method obtained an F-score of 0.674.</abstract>
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%0 Conference Proceedings
%T An LSTM-CRF Based Approach to Token-Level Metaphor Detection
%A Pramanick, Malay
%A Gupta, Ashim
%A Mitra, Pabitra
%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 pramanick-etal-2018-lstm
%X Automatic processing of figurative languages is gaining popularity in NLP community for their ubiquitous nature and increasing volume. In this era of web 2.0, automatic analysis of sarcasm and metaphors is important for their extensive usage. Metaphors are a part of figurative language that compares different concepts, often on a cognitive level. Many approaches have been proposed for automatic detection of metaphors, even using sequential models or neural networks. In this paper, we propose a method for detection of metaphors at the token level using a hybrid model of Bidirectional-LSTM and CRF. We used fewer features, as compared to the previous state-of-the-art sequential model. On experimentation with VUAMC, our method obtained an F-score of 0.674.
%R 10.18653/v1/W18-0908
%U https://aclanthology.org/W18-0908
%U https://doi.org/10.18653/v1/W18-0908
%P 67-75

Markdown (Informal)

[An LSTM-CRF Based Approach to Token-Level Metaphor Detection](https://aclanthology.org/W18-0908) (Pramanick et al., Fig-Lang 2018)

• An LSTM-CRF Based Approach to Token-Level Metaphor Detection (Pramanick et al., Fig-Lang 2018)

ACL

• Malay Pramanick, Ashim Gupta, and Pabitra Mitra. 2018. An LSTM-CRF Based Approach to Token-Level Metaphor Detection. In Proceedings of the Workshop on Figurative Language Processing, pages 67–75, New Orleans, Louisiana. Association for Computational Linguistics.