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 sentimentbased
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.
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CAT s are Fuzzy PETs : A Corpus and Analysis of Potentially Euphemistic 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).
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- Award ID(s):
- 1704113
- PAR ID:
- 10463473
- Date Published:
- Journal Name:
- arXiv preprint arXiv:2205.02728.
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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