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1. Searching for PETs: Using Distributional and Sentiment-Based Methods to Find Potentially Euphemistic Terms

   Gavidia, M. (May 2022, UnImplicit: The Second Workshop on Understanding Implicit and Underspecified Language, co-located with NAACL 2022, Seattle, USA)

   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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2. CAT s are Fuzzy PETs : A Corpus and Analysis of Potentially Euphemistic Terms

   Gavidia, M.; Lee, P.; Feldman, A.; Peng, J. (January 2022, arXiv preprint arXiv:2205.02728.)

   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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3. CS_Morgan at ImageCLEFmedical 2022 Caption Task: Deep Learning Based Multi-Label Classification and Transformers for Concept Detection & Caption Prediction

   Rahman, Md M.; Layode, O. (August 2022, CLEF 2022 – Conference and Labs of the Evaluation Forum, September 5–8, 2022, Bologna, Italy, CEUR Workshop Proceedings (CEUR-WS.org) Proceedings)

   Faggioli, G.; Ferro, N.; Hanbury, A.; Potthast, M. (Ed.)

   This paper describes the participation of Morgan_CS in both Concept Detection and Caption Prediction tasks under the ImageCLEFmedical 2022 Caption task. The task required participants to automatically identifying the presence and location of relevant concepts and composing coherent captions for the entirety of an image in a large corpus which is a subset of the extended Radiology Objects in COntext (ROCO) dataset. Our implementation is motivated by using encoder-decoder based sequence-to-sequence model for caption and concept generation using both pre-trained Text and Vision Transformers (ViTs). In addition, the Concept Detection task is also considered as a multi concept labels classification problem where several deep learning architectures with “sigmoid” activation are used to enable multilabel classification with Keras. We have successfully submitted eight runs for the Concept Detection task and four runs for the Caption Prediction task. For the Concept Detection Task, our best model achieved an F1 score of 0.3519 and for the Caption Prediction Task, our best model achieved a BLEU Score of 0.2549 while using a fusion of Transformers. 

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4. FEED PETs: Further Experimentation and Expansion on the Disambiguation of Potentially Euphemistic Terms.

   Lee P., Shode I. (June 2023, 12th Joint Conference on Lexical and Computational Semantics (SEM 2023))

   Transformers have been shown to work well for the task of English euphemism disambiguation, in which a potentially euphemistic term (PET) is classified as euphemistic or non-euphemistic in a particular context. In this study, we expand on the task in two ways. First, we annotate PETs for vagueness, a linguistic property associated with euphemisms, and find that transformers are generally better at classifying vague PETs, suggesting linguistic differences in the data that impact performance. Second, we present novel euphemism corpora in three different languages: Yoruba, Spanish, and Mandarin Chinese. We perform euphemism disambiguation experiments in each language using multilingual transformer models mBERT and XLM-RoBERTa, establishing preliminary results from which to launch future work. 

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5. Sentence Retrieval for Entity List Extraction with a Seed, Context, and Topic

   https://doi.org/10.1145/3341981.3344250  

   Sarwar, Sheikh Muhammad; Foley, John; Yang, Liu; Allan, James (September 2019, Proceedings of the 2019 International Conference on the Theory of Information Retrieval (ICTIR 2019))

   We present a variation of the corpus-based entity set expansion and entity list completion task. A user-specified query and a sentence containing one seed entity are the input to the task. The output is a list of sentences that contain other instances of the entity class indicated by the input. We construct a semantic query expansion model that leverages topical context around the seed entity and scores sentences. The proposed model finds 46\% of the target entity class by retrieving 20 sentences on average. It achieves 16\% improvement over BM25 model in terms of recall@20. 

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