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pro vyhledávání: '"Feldman, Anna."'
Euphemisms are a form of figurative language relatively understudied in natural language processing. This research extends the current computational work on potentially euphemistic terms (PETs) to Turkish. We introduce the Turkish PET dataset, the fi
Externí odkaz:
http://arxiv.org/abs/2407.13040
Autor:
Ojo, Olumide Ebenezer, Adebanji, Olaronke Oluwayemisi, Gelbukh, Alexander, Calvo, Hiram, Feldman, Anna
Effective communication between healthcare providers and patients is crucial to providing high-quality patient care. In this work, we investigate how Doctor-written and AI-generated texts in healthcare consultations can be classified using state-of-t
Externí odkaz:
http://arxiv.org/abs/2402.04442
Autor:
Lee, Patrick, Trujillo, Alain Chirino, Plancarte, Diana Cuevas, Ojo, Olumide Ebenezer, Liu, Xinyi, Shode, Iyanuoluwa, Zhao, Yuan, Peng, Jing, Feldman, Anna
This study investigates the computational processing of euphemisms, a universal linguistic phenomenon, across multiple languages. We train a multilingual transformer model (XLM-RoBERTa) to disambiguate potentially euphemistic terms (PETs) in multilin
Externí odkaz:
http://arxiv.org/abs/2401.14526
Zero-shot classification enables text to be classified into classes not seen during training. In this study, we examine the efficacy of zero-shot learning models in classifying healthcare consultation responses from Doctors and AI systems. The models
Externí odkaz:
http://arxiv.org/abs/2310.12489
Autor:
Ojo, Olumide E., Adebanji, Olaronke O., Calvo, Hiram, Dieke, Damian O., Ojo, Olumuyiwa E., Akinsanya, Seye E., Abiola, Tolulope O., Feldman, Anna
In this paper, we share our best performing submission to the Arabic AI Tasks Evaluation Challenge (ArAIEval) at ArabicNLP 2023. Our focus was on Task 1, which involves identifying persuasion techniques in excerpts from tweets and news articles. The
Externí odkaz:
http://arxiv.org/abs/2310.09661
Autor:
Lee, Patrick, Shode, Iyanuoluwa, Trujillo, Alain Chirino, Zhao, Yuan, Ojo, Olumide Ebenezer, Plancarte, Diana Cuevas, Feldman, Anna, Peng, Jing
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
Externí odkaz:
http://arxiv.org/abs/2306.00217
Africa has over 2000 indigenous languages but they are under-represented in NLP research due to lack of datasets. In recent years, there have been progress in developing labeled corpora for African languages. However, they are often available in a si
Externí odkaz:
http://arxiv.org/abs/2305.10971
This paper presents The Shared Task on Euphemism Detection for the Third Workshop on Figurative Language Processing (FigLang 2022) held in conjunction with EMNLP 2022. Participants were invited to investigate the euphemism detection task: given input
Externí odkaz:
http://arxiv.org/abs/2211.13327
Publikováno v:
Proceedings of UnImplicit: The Second Workshop on Understanding Implicit and Underspecified Language, NAACL 2022, Seattle
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 simil
Externí odkaz:
http://arxiv.org/abs/2205.10451
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
Externí odkaz:
http://arxiv.org/abs/2205.02728