# Grasping the Finer Point: A Supervised Similarity Network for Metaphor Detection

arXiv: https://arxiv.org/abs/1709.00575 · HTML: https://mlsanity.com/paper/1709.00575/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Sep 2017.

## Built on by (most cited first, 7 of 7)

- [MelBERT: Metaphor Detection via Contextualized Late Interaction using Metaphorical Identification Theories](https://mlsanity.com/paper/2104.13615/) (2021)
- [Construction Artifacts in Metaphor Identification Datasets](https://mlsanity.com/paper/2311.00790/) (2023)
- [How Metaphors Impact Political Discourse: A Large-Scale Topic-Agnostic Study Using Neural Metaphor Detection](https://mlsanity.com/paper/2104.03928/) (2021)
- [Overview of the NLPCC 2024 Shared Task on Chinese Metaphor Generation](https://mlsanity.com/paper/2408.04378/) (2024)
- [Curriculum-style Data Augmentation for LLM-based Metaphor Detection](https://mlsanity.com/paper/2412.02956/) (2024)
- [Combining Pre-trained Word Embeddings and Linguistic Features for Sequential Metaphor Identification](https://mlsanity.com/paper/2104.03285/) (2021)
- [SASICM A Multi-Task Benchmark For Subtext Recognition](https://mlsanity.com/paper/2106.06944/) (2021)

## Abstract (arXiv)

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.