Searching for PET s: Using Distributional and Sentiment-Based Methods to Find Potentially Euphemistic Terms (2022)

Abstract: 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 distri- butional 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.

## introduction euphemisms are mild or indirect expressions used in place of harsher or more offensive ones. they can be used to show politeness when discussing sensitive or taboo topics @xcite such as saying passed away instead of died, or as a way to make unpleasant or unappealing things sound better @xcite , such as ethnic cleansing instead of genocide. they can even be used as a means to conceal the truth @xcite ; for example, saying enhanced interrogation techniques but meaning torture. euphemisms pose a challenge to natural language processing due to this figurative behavior, but also because they can have a literal interpretation in certain contexts. furthermore, humans may not agree on what a euphemism is @xcite . thus, we consider any words/phrases used in this nature to be a potentially euphemistic term (pet). in this paper, we present a proof of concept for finding pets in an input sentence, and apply it to a novel euphemism corpus @xcite foot_0 . we base our approach on several linguistic intuitions: (1) pets tend to be commonly used expressions about a certain range of sensitive topics, (2) humans make a conscious lexical choice to convey politeness and formality and (3) because of their linguistic function, pets should result in greater sentiment shifts when replaced by their literal interpretations; we experiment with distributionally similar alternatives as a source of such interpretations. leveraging a variety of existing tools (gensim's phrases @xcite , word2vec classes @xcite , and roberta @xcite ), we implement a simple algorithm to extract, filter, and rank pet candidates. despite its simplicity, our approach is able to identify the target euphemism as one of the top two phrase candidates for 725 out of 1382 sentences in our test dataset. it also shows promising results in identifying pets that were not originally marked, as well as for sentences outside our dataset. we believe our results and subsequent discussion are an important baseline for using distributional and sentiment-based methods for detecting euphemisms. the structure of the paper is as follows: in section 2, we discuss related work surrounding euphemisms. section 3 provides details on the text data used, section 4 describes our approach broken down into 4 stages: phrase extraction, phrase filtering, phrase paraphrasing and phrase ranking. section 5 includes our results and a quantitative and qualitative analysis, and section 6 concludes with future work. ## related work computational approaches to processing euphemisms @xcite @xcite @xcite have shown much promise, but the dynamic nature of euphemisms remains an obstacle. a euphemism annotation task conducted by @xcite shows that the inherent ambiguity of euphemisms leads to low agreement in what qualifies as a euphemism. through this task, the researchers found that some euphemisms are used so often to discuss sensitive topics (e.g., venereal disease as a euphemism for sexually transmitted disease), that they become commonly accepted terms, or cats. additionally, they find that even when annotators agreed on the intended meaning of a euphemism, e.g. slim as a euphemism for skinny, they still did not agree on the label of euphemistic vs. non euphemistic. the nuance associated with euphemisms still remains one of the biggest challenges. felt and riloff (2020) were one of the first to tackle euphemisms from a computational standpoint. they leverage sentiment analysis to recognize x-phemisms, which is the term they use to refer to both euphemisms and dysphemisms. whereas euphemisms are polite expressions to discuss sensitive topics, dysphemisms are purposely direct, blunt and can be derogatory. they find near-synonym pairs for three topics: lying, firing and stealing, and use a weakly supervised bootstrapping algorithm for semantic lexicon induction @xcite . they use lexical cues and sentiment analysis to classify phrases as euphemistic, dysphemistic or neutral. their approach is interesting, as it is the first of its kind and their use of sentiment analysis to identify euphemisms has inspired our work. @xcite approach the task of discovering euphemisms from the lens of content moderation. their goal was the detection of euphemisms used for formal drug names on social media. they define two problems: the first is the detection of euphemisms, and the second is identifying what the euphemisms found actually refer to. however, their view on euphemisms is different from ours, as they treat euphemisms simply as code words. this work is similar to @xcite , who also explore euphemisms as code words in hate speech. @xcite and @xcite both treat detection and identification as a masked language problem where they use a masked language model (mlm) as a filter to get rid of sentences that are not related to their seedlist of euphemisms and then again to find euphemistic candidates. like felt and riloff (2020), @xcite and @xcite show promise, though their narrow topic focus limit the kinds of euphemisms that can be found. lastly, @xcite conduct a diachronic evaluation of euphemism usage between genders. while this work is not aimed at finding euphemisms, their work provides many of the pets used in the creation of the euphemism corpus @xcite , which we use in this paper. ## data our work utilizes a euphemism corpus created by @xcite as our test data. the raw text data for this corpus comes from the corpus of global web-based english (glowbe) @xcite . glowbe contains text data for 20 english speaking countries from websites, blogs and forums; this corpus is compiled using just a portion of the us dialect of english text. the euphemism corpus contains 1,382 euphemistic sentences, each annotated with one potentially euphemistic term per sentence. these potentially euphemistic terms, or pets @xcite are single and multi word expressions that are used in a euphemistic sense. futhermore, we use the us dialect of english portion of glowbe to train a phrases model (gensim) @xcite to create word collocations within our data which are then fed into a word2vec model to produce vector representations for the words in our corpus. the following section explains both of these aspects in further detail. ## our approach the algorithm developed for this experiment performs the following sub tasks to identify a pet in a sentence: phrase extraction, phrase filtering, phrase paraphrasing and phrase ranking. simply put, the algorithm locates all of the single and multi word expressions within a sentence and through the subsequent tasks, determines which expressions may be a pet. ## conclusion and future work our work is a proof of concept for finding pets in a given euphemistic sentence. while our algorithm produces significant results, we recognize the limitations of our work and propose the following ideas for advancement of this specific task. firstly, we rely on the gensim library for identifying multiword expressions and obtaining word embeddings, but experimentation with different parameters and techniques (e.g., using different phrase extraction methods, different bigram scoring functions or contextualized word embeddings) may yield better results. secondly, a mechanism for filtering each candidate's alternatives could help reduce the number of semantically dissimilar replacements during the paraphrasing stage. next, while we only use aggregate increases in sentiment and offensiveness scores for ranking candidates, a variety of other methods (e.g., taking averages or maximums) and measures (e.g., indirectness and vagueness) may be useful for distinguishing pets. lastly, while the task of differentiating literal versus euphemistic usages of pets is not a focus on this paper, our algorithm shows some promise on the issue (see appendix e), and it is an important task that could use future work; appendix e also shows the performance of our algorithm on unseen data. since our algorithm works by placing a candidate pet in one of the top two rankings, we evaluated the results and found that new pets were found and correctly placed in top ranking positions. one of the limitations of the euphemism corpus is that it only includes one annotated pet per sentence, our algorithm shows potential to expand upon the annotations in the corpus to include the new pets found. we underline the new pets in the examples below as well as provide our interpretations. example 1 sentence: or acknowledge real-world trade-offs such as the strong likelihood of amount of of civilian casualties if aq detainees were treated according to either geneva convention or uc criminal law standards rankedphrases: [('civilian casualties', 3.9511645138263702), ('criminal law', 2.082954853773117), ('trade-offs', 2.0316174626350403), ('geneva convention', 1.7544293403625488), ('acknowledge', 1.5634678304195404), ('detainees were', 1.2355359494686127), ('treated', 1.2001541256904602), ('standards', 0.8081734478473663)] new pet: civilian casualties interpretation: the unintended deaths of civilians example 2 sentence: pelosi says she was briefed by bush administration officials on the legal justification for using waterboarding but that they never followed through on promises to inform her when they actually began using enhanced interrogation techniques rankedphrases: [('using waterboarding', 6.236076384782791), ('enhanced interrogation techniques', 3.640248477458954), ('she was', 1.1687388718128204), ('legal justification', 1.1285315454006195), ('when they', 0.9696991741657257)] new pet: using waterboarding interpretation: a form of torture where a person is strapped down to a board and water is poured over their face in a way that is similar to drowning example 3 sentence: religious people often complain that secular therapists see their faith as a problem or a symptom rather than as a conviction to be respected and incorporated into the therapeutic dialogue a concern that is especially pronounced among the elderly and twentysomethings rankedphrases: