CATs are Fuzzy PETs: A Corpus and Analysis of Potentially Euphemistic Terms
This addresses the challenge of modeling polite and figurative language for NLP applications, but it is incremental as it focuses on data collection and preliminary analysis rather than novel methods.
The paper tackles the understudied problem of euphemisms in natural language processing by creating a corpus of potentially euphemistic terms (PETs) and analyzing them, finding that PETs generally reduce negative sentiment in texts and highlighting human disagreement in annotation due to factors like commonly accepted terms.
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).