Grasping the Finer Point: A Supervised Similarity Network for Metaphor Detection
This addresses the problem of metaphor processing for natural language understanding, offering a novel method to replace hand-engineered features.
The paper tackled metaphor detection in natural language understanding by introducing the first deep learning architecture to capture metaphorical composition, which outperformed existing approaches.
The ubiquity of metaphor in our everyday communication makes it an important problem for natural language understanding. Yet, the majority of metaphor processing systems to date rely on hand-engineered features and there is still no consensus in the field as to which features are optimal for this task. In this paper, we present the first deep learning architecture designed to capture metaphorical composition. Our results demonstrate that it outperforms the existing approaches in the metaphor identification task.