Abstract
This paper presents a linguistically driven proof of concept for finding potentially euphemistic terms, or PETs. Acknowledging that PETs tend to be commonly used expressions for a certain range of sensitive topics, we make use of distri- butional similarities to select and filter phrase candidates from a sentence and rank them using a set of simple sentiment-based metrics. We present the results of our approach tested on a corpus of sentences containing euphemisms, demonstrating its efficacy for detecting single and multi-word PETs from a broad range of topics. We also discuss future potential for sentiment-based methods on this task.- Anthology ID:
- 2022.unimplicit-1.4
- Volume:
- Proceedings of the Second Workshop on Understanding Implicit and Underspecified Language
- Month:
- July
- Year:
- 2022
- Address:
- Seattle, USA
- Editors:
- Valentina Pyatkin, Daniel Fried, Talita Anthonio
- Venue:
- unimplicit
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 22–32
- Language:
- URL:
- https://aclanthology.org/2022.unimplicit-1.4
- DOI:
- 10.18653/v1/2022.unimplicit-1.4
- Cite (ACL):
- Patrick Lee, Martha Gavidia, Anna Feldman, and Jing Peng. 2022. Searching for PETs: Using Distributional and Sentiment-Based Methods to Find Potentially Euphemistic Terms. In Proceedings of the Second Workshop on Understanding Implicit and Underspecified Language, pages 22–32, Seattle, USA. Association for Computational Linguistics.
- Cite (Informal):
- Searching for PETs: Using Distributional and Sentiment-Based Methods to Find Potentially Euphemistic Terms (Lee et al., unimplicit 2022)
- PDF:
- https://preview.aclanthology.org/bionlp-24-ingestion/2022.unimplicit-1.4.pdf
- Code
- marsgav/petdetection