@inproceedings{biyik-etal-2024-turkish,
title = "{T}urkish Delights: a Dataset on {T}urkish Euphemisms",
author = "Biyik, Hasan and
Lee, Patrick and
Feldman, Anna",
editor = {Ataman, Duygu and
Derin, Mehmet Oguz and
Ivanova, Sardana and
K{\"o}ksal, Abdullatif and
S{\"a}lev{\"a}, Jonne and
Zeyrek, Deniz},
booktitle = "Proceedings of the First Workshop on Natural Language Processing for Turkic Languages (SIGTURK 2024)",
month = aug,
year = "2024",
address = "Bangkok, Thailand and Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.sigturk-1.7",
pages = "71--80",
abstract = "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 first available of its kind in the field. By creating a list of euphemisms in Turkish, collecting example contexts, and annotating them, we provide both euphemistic and non-euphemistic examples of PETs in Turkish. We describe the dataset and methodologies, and also experiment with transformer-based models on Turkish euphemism detection by using our dataset for binary classification. We compare performances across models using F1, accuracy, and precision as evaluation metrics.",
}
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<abstract>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 first available of its kind in the field. By creating a list of euphemisms in Turkish, collecting example contexts, and annotating them, we provide both euphemistic and non-euphemistic examples of PETs in Turkish. We describe the dataset and methodologies, and also experiment with transformer-based models on Turkish euphemism detection by using our dataset for binary classification. We compare performances across models using F1, accuracy, and precision as evaluation metrics.</abstract>
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%0 Conference Proceedings
%T Turkish Delights: a Dataset on Turkish Euphemisms
%A Biyik, Hasan
%A Lee, Patrick
%A Feldman, Anna
%Y Ataman, Duygu
%Y Derin, Mehmet Oguz
%Y Ivanova, Sardana
%Y Köksal, Abdullatif
%Y Sälevä, Jonne
%Y Zeyrek, Deniz
%S Proceedings of the First Workshop on Natural Language Processing for Turkic Languages (SIGTURK 2024)
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand and Online
%F biyik-etal-2024-turkish
%X 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 first available of its kind in the field. By creating a list of euphemisms in Turkish, collecting example contexts, and annotating them, we provide both euphemistic and non-euphemistic examples of PETs in Turkish. We describe the dataset and methodologies, and also experiment with transformer-based models on Turkish euphemism detection by using our dataset for binary classification. We compare performances across models using F1, accuracy, and precision as evaluation metrics.
%U https://aclanthology.org/2024.sigturk-1.7
%P 71-80
Markdown (Informal)
[Turkish Delights: a Dataset on Turkish Euphemisms](https://aclanthology.org/2024.sigturk-1.7) (Biyik et al., SIGTURK-WS 2024)
ACL
- Hasan Biyik, Patrick Lee, and Anna Feldman. 2024. Turkish Delights: a Dataset on Turkish Euphemisms. In Proceedings of the First Workshop on Natural Language Processing for Turkic Languages (SIGTURK 2024), pages 71–80, Bangkok, Thailand and Online. Association for Computational Linguistics.