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.
Cite
CITATION STYLE
Bizzoni, Y., & Ghanimifard, M. (2018). Bigrams and BiLSTMs two neural networks for sequential metaphor detection. In Proceedings of the Workshop on Figurative Language Processing, Fig-Lang 2018 at the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HTL 2018 (pp. 91–101). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/w18-0911
Register to see more suggestions
Mendeley helps you to discover research relevant for your work.
