# Metaphor Detection via Explicit Basic Meanings Modelling

arXiv: https://arxiv.org/abs/2305.17268 · HTML: https://mlsanity.com/paper/2305.17268/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 26 references that are in the map.
- First posted on arXiv 26 May 2023.

## Builds on (most cited first, 6 of 6)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [MelBERT: Metaphor Detection via Contextualized Late Interaction using Metaphorical Identification Theories](https://mlsanity.com/paper/2104.13615/) (2021)
- [Neural Metaphor Detection in Context](https://mlsanity.com/paper/1808.09653/) (2018)
- [FrameBERT: Conceptual Metaphor Detection with Frame Embedding Learning](https://mlsanity.com/paper/2302.04834/) (2023)
- [Metaphor Detection with Effective Context Denoising](https://mlsanity.com/paper/2302.05611/) (2023)
- [The Secret of Metaphor on Expressing Stronger Emotion](https://mlsanity.com/paper/2301.13042/) (2023)

## Built on by (most cited first, 12 of 12)

- [Multi-Granular Multimodal Clue Fusion for Meme Understanding](https://mlsanity.com/paper/2503.12560/) (2025)
- [CMDAG: A Chinese Metaphor Dataset with Annotated Grounds as CoT for Boosting Metaphor Generation](https://mlsanity.com/paper/2402.13145/) (2024)
- [CoMet: Metaphor-Driven Covert Communication for Multi-Agent Language Games](https://mlsanity.com/paper/2505.18218/) (2025)
- [Meta4XNLI: A Crosslingual Parallel Corpus for Metaphor Detection and Interpretation](https://mlsanity.com/paper/2404.07053/) (2024)
- [Finding Challenging Metaphors that Confuse Pretrained Language Models](https://mlsanity.com/paper/2401.16012/) (2024)
- [Concept Drift Guided LayerNorm Tuning for Efficient Multimodal Metaphor Identification](https://mlsanity.com/paper/2505.11237/) (2025)
- [An Expectation-Realization Model for Metaphor Detection](https://mlsanity.com/paper/2311.03963/) (2023)
- [Overview of the NLPCC 2024 Shared Task on Chinese Metaphor Generation](https://mlsanity.com/paper/2408.04378/) (2024)
- [Deep Learning-Based Knowledge Injection for Metaphor Detection: A Comprehensive Review](https://mlsanity.com/paper/2308.04306/) (2023)
- [Learning the Cue or Learning the Word? Analyzing Generalization in Metaphor Detection for Verbs](https://mlsanity.com/paper/2604.13713/) (2026)
- [Towards High-Level Semantic Intelligence](https://mlsanity.com/paper/2607.24082/) (2026)
- [Cross-Dataset Stability of Expert-Informed Skill Prompting and Fine-Tuning for Chinese Metaphor Identification](https://mlsanity.com/paper/2608.25579/) (2026)

## Abstract (arXiv)

One noticeable trend in metaphor detection is the embrace of linguistic theories such as the metaphor identification procedure (MIP) for model architecture design. While MIP clearly defines that the metaphoricity of a lexical unit is determined based on the contrast between its textitcontextual meaning and its textitbasic meaning, existing work does not strictly follow this principle, typically using the textitaggregated meaning to approximate the basic meaning of target words. In this paper, we propose a novel metaphor detection method, which models the basic meaning of the word based on literal annotation from the training set, and then compares this with the contextual meaning in a target sentence to identify metaphors. Empirical results show that our method outperforms the state-of-the-art method significantly by 1.0% in F1 score. Moreover, our performance even reaches the theoretical upper bound on the VUA18 benchmark for targets with basic annotations, which demonstrates the importance of modelling basic meanings for metaphor detection.