---
title: "Not all ANIMALs are equal — paper by Otmakhova et al. (2026)"
description: "Metaphor studies in NLP usually label a metaphor with its source domain, such as WATER or ANIMAL. 84 references."
authors: ["Yulia Otmakhova", "Matteo Guida", "Lea Frermann"]
year: 2026
venue: "Findings of the Association for Computational Linguistics: ACL 2026"
source: "https://aclanthology.org/2026.findings-acl.1911/"
doi: "10.18653/v1/2026.findings-acl.1911"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/otmakhova-2026-not-all-animals-are-equal/"
---
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# Not all ANIMALs are equal: metaphorical framing through source domains and semantic frames

**Authors:** [Yulia Otmakhova](https://md.research.elamur.ai/free/en/authors/yulia-otmakhova/), [Matteo Guida](https://md.research.elamur.ai/free/en/authors/matteo-guida/) and [Lea Frermann](https://md.research.elamur.ai/free/en/authors/lea-frermann/) · **Year:** 2026 · **Venue:** [Findings of the Association for Computational Linguistics: ACL 2026](https://md.research.elamur.ai/free/en/venues/findings/) ([Findings of the Association for Computational Linguistics 2026](https://md.research.elamur.ai/free/en/venues/findings/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.findings-acl.1911/) · [PDF](https://aclanthology.org/2026.findings-acl.1911.pdf) · [DOI 10.18653/v1/2026.findings-acl.1911](https://doi.org/10.18653/v1/2026.findings-acl.1911) · [OpenAlex](https://openalex.org/W7166802854)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary
Metaphor studies in NLP usually label a metaphor with its source domain, such as WATER or ANIMAL. A source domain alone cannot explain why a metaphor carries a particular connotation. Calling migrants a "flood" and calling them "waves" both draw on WATER, yet they suggest very different attitudes. The authors argue that the specific association is selected by the FrameNet semantic frame of the word used.

They built ConceptFrameMet, a RoBERTa-based system with three parts:
- a metaphor detector,
- a source domain classifier,
- a semantic frame classifier.

A log-likelihood ratio test then compares a target corpus with a background corpus. This identifies which domain–frame pairs are salient, meaning they act as discourse metaphors rather than one-off rhetoric.

The framework was applied to climate change news and to immigration tweets. On the tweets, conservatives and liberals used the same source domains but different frames. Conservatives preferred frames stressing loss of control, while liberals chose neutral or victim-oriented ones.

An end-to-end LLM baseline hallucinated frames, which supports the case for this grounded, statistically testable method.

## Key points
- **Semantic frame classifier:** trained on FrameNet 1.7 (797 frames; 19391/2272/6714 train/dev/test samples). The best setup, RoBERTa SEP, marks the target word with separators and scores about 0.86 micro-F1 and 0.648 macro-F1. These scores approach those of earlier systems by Devasier et al. (2024) and An et al. (2023), which rely heavily on data augmentation.
- **LLM comparison on frame prediction:** prompted LLMs did worse, with Gemini 2.5 at about 0.51 and Claude Sonnet 4.0 at about 0.74 micro-F1.
- **Source domains and saliency:** source domain labels come from the LCC corpus (Mohler et al., 2016). Saliency uses the log-likelihood ratio method of Rayson and Garside (2000).
- **Released resources:** a new climate change metaphor corpus annotated with source domains, plus code and models, available at github.com/julia-nixie/ConceptFrameMet.
- **End-to-end LLM test:** Claude Sonnet 4.0 was asked to run the whole pipeline on WATER and ANIMAL tweets. It invented frames and examples and produced similar output even for texts without such metaphors. This suggests it relied on prior knowledge rather than the input corpus.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

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## Cite

Cite as: Yulia Otmakhova, Matteo Guida and Lea Frermann. 2026. Not all ANIMALs are equal: metaphorical framing through source domains and semantic frames. Findings of the Association for Computational Linguistics: ACL 2026.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{otmakhova2026not,
  title = {{Not all ANIMALs are equal: metaphorical framing through source domains and semantic frames}},
  author = {Yulia Otmakhova, Matteo Guida and Lea Frermann},
  booktitle = {Findings of the Association for Computational Linguistics: ACL 2026},
  year = {2026},
  doi = {10.18653/v1/2026.findings-acl.1911},
  url = {https://doi.org/10.18653/v1/2026.findings-acl.1911}
}
```

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*Updated 1 November 2026*