# CATs are Fuzzy PETs: A Corpus and Analysis of Potentially Euphemistic Terms

arXiv: https://arxiv.org/abs/2205.02728 · HTML: https://mlsanity.com/paper/2205.02728/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 41 references that are in the map.
- First posted on arXiv 5 May 2022.

## Builds on (most cited first, 9 of 9)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [TweetEval: Unified Benchmark and Comparative Evaluation for Tweet Classification](https://mlsanity.com/paper/2010.12421/) (2020)
- [A Computational Approach to Politeness with Application to Social Factors](https://mlsanity.com/paper/1306.6078/) (2013)
- [Politeness Transfer: A Tag and Generate Approach](https://mlsanity.com/paper/2004.14257/) (2020)
- [How should my chatbot interact? A survey on human-chatbot interaction design](https://mlsanity.com/paper/1904.02743/) (2019)
- [Self-Supervised Euphemism Detection and Identification for Content Moderation](https://mlsanity.com/paper/2103.16808/) (2021)
- [Euphemistic Phrase Detection by Masked Language Model](https://mlsanity.com/paper/2109.04666/) (2021)
- [Understanding Chatbot-mediated Task Management](https://mlsanity.com/paper/1802.03109/) (2018)
- [A diachronic evaluation of gender asymmetry in euphemism](https://mlsanity.com/paper/2106.02083/) (2021)

## Built on by (most cited first, 14 of 14)

- [Searching for PETs: Using Distributional and Sentiment-Based Methods to Find Potentially Euphemistic Terms](https://mlsanity.com/paper/2205.10451/) (2022)
- [A Report on the Euphemisms Detection Shared Task](https://mlsanity.com/paper/2211.13327/) (2022)
- [EUREKA: EUphemism Recognition Enhanced through Knn-based methods and Augmentation](https://mlsanity.com/paper/2210.12846/) (2022)
- [Exploring Euphemism Detection in Few-Shot and Zero-Shot Settings](https://mlsanity.com/paper/2210.12926/) (2022)
- [Detecting Euphemisms with Literal Descriptions and Visual Imagery](https://mlsanity.com/paper/2211.04576/) (2022)
- [FEED PETs: Further Experimentation and Expansion on the Disambiguation of Potentially Euphemistic Terms](https://mlsanity.com/paper/2306.00217/) (2023)
- [MEDs for PETs: Multilingual Euphemism Disambiguation for Potentially Euphemistic Terms](https://mlsanity.com/paper/2401.14526/) (2024)
- [Turkish Delights: a Dataset on Turkish Euphemisms](https://mlsanity.com/paper/2407.13040/) (2024)
- [When Does Language Transfer Help? Sequential Fine-Tuning for Cross-Lingual Euphemism Detection](https://mlsanity.com/paper/2508.11831/) (2025)
- [TEDB System Description to a Shared Task on Euphemism Detection 2022](https://mlsanity.com/paper/2301.06602/) (2023)
- [Computational Approaches to the Detection of Lesser-Known Rhetorical Figures: A Systematic Survey and Research Challenges](https://mlsanity.com/paper/2406.16674/) (2024)
- [Enhancing Rhetorical Figure Annotation: An Ontology-Based Web Application with RAG Integration](https://mlsanity.com/paper/2412.13799/) (2024)
- [When Semantic Overlap Is Not Enough: Cross-Lingual Euphemism Transfer Between Turkish and English](https://mlsanity.com/paper/2602.16957/) (2026)
- [Diverse Word Choices, Same Reference: Annotating Lexically-Rich Cross-Document Coreference](https://mlsanity.com/paper/2602.17424/) (2026)

## Abstract (arXiv)

Euphemisms have not received much attention in natural language processing, despite being an important element of polite and figurative language. Euphemisms prove to be a difficult topic, not only because they are subject to language change, but also because humans may not agree on what is a euphemism and what is not. Nevertheless, the first step to tackling the issue is to collect and analyze examples of euphemisms. We present a corpus of potentially euphemistic terms (PETs) along with example texts from the GloWbE corpus. Additionally, we present a subcorpus of texts where these PETs are not being used euphemistically, which may be useful for future applications. We also discuss the results of multiple analyses run on the corpus. Firstly, we find that sentiment analysis on the euphemistic texts supports that PETs generally decrease negative and offensive sentiment. Secondly, we observe cases of disagreement in an annotation task, where humans are asked to label PETs as euphemistic or not in a subset of our corpus text examples. We attribute the disagreement to a variety of potential reasons, including if the PET was a commonly accepted term (CAT).