Searching for PETs: Using Distributional and Sentiment-Based Methods to Find Potentially Euphemistic Terms
Patrick P. C. Lee, Martha Gavidia, Anna Feldman, Jing Peng
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 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.
Topics & Concepts
Computer scienceSentenceNatural language processingPhraseSet (abstract data type)Rank (graph theory)Task (project management)Artificial intelligenceFilter (signal processing)Range (aeronautics)Word (group theory)Simple (philosophy)Sentiment analysisLinguisticsMathematicsEpistemologyEconomicsPhilosophyCombinatoricsComputer visionComposite materialProgramming languageMaterials scienceManagementHate Speech and Cyberbullying DetectionSwearing, Euphemism, MultilingualismNatural Language Processing Techniques