Searching for PETs: Using Distributional and Sentiment-Based Methods to Find Potentially Euphemistic Terms
This addresses the challenge of automatically detecting euphemisms in text for natural language processing applications, but it is incremental as it builds on existing linguistic and sentiment methods.
The paper tackled the problem of identifying potentially euphemistic terms (PETs) by using distributional similarities and sentiment-based metrics to rank phrase candidates, demonstrating efficacy in detecting single and multi-word PETs across a broad range of topics.
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.