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This paper presents a linguistically driven proof of concept for findingpotentially euphemistic terms, or PETs. Acknowledging that PETs tend to becommonly used expressions for a certain range of sensitive topics, we make useof distributional similarities to select and filter phrase candidates from asentence and rank them using a set of simple sentiment-based metrics. Wepresent the results of our approach tested on a corpus of sentences containingeuphemisms, demonstrating its efficacy for detecting single and multi-word PETsfrom a broad range of topics. We also discuss future potential forsentiment-based methods on this task.

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