2019
ACL
ACL 2019
End-to-End Sequential Metaphor Identification Inspired by Linguistic Theories
Abstract
AbstractEnd-to-end training with Deep Neural Networks (DNN) is a currently popular method for metaphor identification. However, standard sequence tagging models do not explicitly take advantage of linguistic theories of metaphor identification. We experiment with two DNN models which are inspired by two human metaphor identification procedures. By testing on three public datasets, we find that our models achieve state-of-the-art performance in end-to-end metaphor identification.
🌉
Interdisciplinary Bridge
- Artificial Intelligence and Natural Language Processing
Authors
Topics
Artificial Intelligence > Core AI > Interpretability
Machine Learning > Core Methods > Classification
Deep Learning > Architectures > Neural Networks
Natural Language Processing > Understanding > Named Entity Recognition
Natural Language Processing > Understanding > Semantic Analysis
Deep Learning > Learning Types > Deep Learning
Machine Learning > Core Methods > Sequence Labeling
Machine Learning > Core Methods > Sequence Modeling