Searching for PETs: Using Distributional and Sentiment-Based Methods to Find Potentially Euphemistic Terms

Autor:	Lee, Patrick, Gavidia, Martha, Feldman, Anna, Peng, Jing
Rok vydání:	2022
Předmět:	Computer Science - Computation and Language
Zdroj:	Proceedings of UnImplicit: The Second Workshop on Understanding Implicit and Underspecified Language, NAACL 2022, Seattle
Druh dokumentu:	Working Paper
Popis:	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 distributional 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.
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2205.10451 Zobrazit plný text záznamu View this record from Arxiv