Bigrams and BiLSTMs Two Neural Networks for Sequential Metaphor Detection

Yuri Bizzoni; Mehdi Ghanimifard

doi:10.18653/v1/W18-0911

Bigrams and BiLSTMs Two Neural Networks for Sequential Metaphor Detection

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Abstract

We present and compare two alternative deep neural architectures to perform word-level metaphor detection on text: a bi-LSTM model and a new structure based on recursive feed-forward concatenation of the input. We discuss different versions of such models and the effect that input manipulation - specifically, reducing the length of sentences and introducing concreteness scores for words - have on their performance.

Anthology ID:: W18-0911
Volume:: Proceedings of the Workshop on Figurative Language Processing
Month:: June
Year:: 2018
Address:: New Orleans, Louisiana
Editors:: Beata Beigman Klebanov, Ekaterina Shutova, Patricia Lichtenstein, Smaranda Muresan, Chee Wee
Venue:: Fig-Lang
SIG:
Publisher:: Association for Computational Linguistics
Note:
Pages:: 91–101
Language:
URL:: https://aclanthology.org/W18-0911/
DOI:: 10.18653/v1/W18-0911
Bibkey:
Cite (ACL):: Yuri Bizzoni and Mehdi Ghanimifard. 2018. Bigrams and BiLSTMs Two Neural Networks for Sequential Metaphor Detection. In Proceedings of the Workshop on Figurative Language Processing, pages 91–101, New Orleans, Louisiana. Association for Computational Linguistics.
Cite (Informal):: Bigrams and BiLSTMs Two Neural Networks for Sequential Metaphor Detection (Bizzoni & Ghanimifard, Fig-Lang 2018)
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PDF:: https://aclanthology.org/W18-0911.pdf

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@inproceedings{bizzoni-ghanimifard-2018-bigrams,
    title = "Bigrams and {B}i{LSTM}s Two Neural Networks for Sequential Metaphor Detection",
    author = "Bizzoni, Yuri  and
      Ghanimifard, Mehdi",
    editor = "Beigman Klebanov, Beata  and
      Shutova, Ekaterina  and
      Lichtenstein, Patricia  and
      Muresan, Smaranda  and
      Wee, Chee",
    booktitle = "Proceedings of the Workshop on Figurative Language Processing",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/W18-0911/",
    doi = "10.18653/v1/W18-0911",
    pages = "91--101",
    abstract = "We present and compare two alternative deep neural architectures to perform word-level metaphor detection on text: a bi-LSTM model and a new structure based on recursive feed-forward concatenation of the input. We discuss different versions of such models and the effect that input manipulation - specifically, reducing the length of sentences and introducing concreteness scores for words - have on their performance."
}

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%0 Conference Proceedings
%T Bigrams and BiLSTMs Two Neural Networks for Sequential Metaphor Detection
%A Bizzoni, Yuri
%A Ghanimifard, Mehdi
%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 bizzoni-ghanimifard-2018-bigrams
%X We present and compare two alternative deep neural architectures to perform word-level metaphor detection on text: a bi-LSTM model and a new structure based on recursive feed-forward concatenation of the input. We discuss different versions of such models and the effect that input manipulation - specifically, reducing the length of sentences and introducing concreteness scores for words - have on their performance.
%R 10.18653/v1/W18-0911
%U https://aclanthology.org/W18-0911/
%U https://doi.org/10.18653/v1/W18-0911
%P 91-101

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Markdown (Informal)

[Bigrams and BiLSTMs Two Neural Networks for Sequential Metaphor Detection](https://aclanthology.org/W18-0911/) (Bizzoni & Ghanimifard, Fig-Lang 2018)

Bigrams and BiLSTMs Two Neural Networks for Sequential Metaphor Detection (Bizzoni & Ghanimifard, Fig-Lang 2018)

ACL

Yuri Bizzoni and Mehdi Ghanimifard. 2018. Bigrams and BiLSTMs Two Neural Networks for Sequential Metaphor Detection. In Proceedings of the Workshop on Figurative Language Processing, pages 91–101, New Orleans, Louisiana. Association for Computational Linguistics.