@inproceedings{manoleasa-etal-2022-fii,
title = "{FII} {UAIC} at {S}em{E}val-2022 Task 6: i{S}arcasm{E}val - Intended Sarcasm Detection in {E}nglish and {A}rabic",
author = "Manoleasa, Tudor and
Gifu, Daniela and
Sandu, Iustin",
editor = "Emerson, Guy and
Schluter, Natalie and
Stanovsky, Gabriel and
Kumar, Ritesh and
Palmer, Alexis and
Schneider, Nathan and
Singh, Siddharth and
Ratan, Shyam",
booktitle = "Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.semeval-1.136",
doi = "10.18653/v1/2022.semeval-1.136",
pages = "970--977",
abstract = "The {``}iSarcasmEval - Intended Sarcasm Detection in English and Arabic{''} task at the SemEval 2022 competition focuses on detectingand rating the distinction between intendedand perceived sarcasm in the context of textual sarcasm detection, as well as the level ofirony contained in these texts. In the contextof SemEval, we present a binary classificationmethod which classifies the text as sarcasticor non-sarcastic (task A, for English) based onfive classical machine learning approaches bytrying to train the models based on this datasetsolely (i.e., no other datasets have been used).This process indicates low performance compared to previously studied datasets, which in2dicates that the previous ones might be biased.",
}
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<abstract>The “iSarcasmEval - Intended Sarcasm Detection in English and Arabic” task at the SemEval 2022 competition focuses on detectingand rating the distinction between intendedand perceived sarcasm in the context of textual sarcasm detection, as well as the level ofirony contained in these texts. In the contextof SemEval, we present a binary classificationmethod which classifies the text as sarcasticor non-sarcastic (task A, for English) based onfive classical machine learning approaches bytrying to train the models based on this datasetsolely (i.e., no other datasets have been used).This process indicates low performance compared to previously studied datasets, which in2dicates that the previous ones might be biased.</abstract>
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%0 Conference Proceedings
%T FII UAIC at SemEval-2022 Task 6: iSarcasmEval - Intended Sarcasm Detection in English and Arabic
%A Manoleasa, Tudor
%A Gifu, Daniela
%A Sandu, Iustin
%Y Emerson, Guy
%Y Schluter, Natalie
%Y Stanovsky, Gabriel
%Y Kumar, Ritesh
%Y Palmer, Alexis
%Y Schneider, Nathan
%Y Singh, Siddharth
%Y Ratan, Shyam
%S Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, United States
%F manoleasa-etal-2022-fii
%X The “iSarcasmEval - Intended Sarcasm Detection in English and Arabic” task at the SemEval 2022 competition focuses on detectingand rating the distinction between intendedand perceived sarcasm in the context of textual sarcasm detection, as well as the level ofirony contained in these texts. In the contextof SemEval, we present a binary classificationmethod which classifies the text as sarcasticor non-sarcastic (task A, for English) based onfive classical machine learning approaches bytrying to train the models based on this datasetsolely (i.e., no other datasets have been used).This process indicates low performance compared to previously studied datasets, which in2dicates that the previous ones might be biased.
%R 10.18653/v1/2022.semeval-1.136
%U https://aclanthology.org/2022.semeval-1.136
%U https://doi.org/10.18653/v1/2022.semeval-1.136
%P 970-977
Markdown (Informal)
[FII UAIC at SemEval-2022 Task 6: iSarcasmEval - Intended Sarcasm Detection in English and Arabic](https://aclanthology.org/2022.semeval-1.136) (Manoleasa et al., SemEval 2022)
ACL