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
- 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)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/W18-0911.pdf
- Code
- GU-CLASP/ocota