@inproceedings{delil-kuyumcu-2023-sefamerve,
title = "Sefamerve at {S}em{E}val-2023 Task 12: Semantic Evaluation of Rarely Studied Languages",
author = "Delil, Selman and
Kuyumcu, Birol",
editor = {Ojha, Atul Kr. and
Do{\u{g}}ru{\"o}z, A. Seza and
Da San Martino, Giovanni and
Tayyar Madabushi, Harish and
Kumar, Ritesh and
Sartori, Elisa},
booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.semeval-1.71",
doi = "10.18653/v1/2023.semeval-1.71",
pages = "512--516",
abstract = "This paper describes our contribution to SemEval-23 Shared Task 12: ArfiSenti. The task consists of several sentiment classification subtasks for rarely studied African languages to predict positive, negative, or neutral classes of a given Twitter dataset. In our system we utilized three different models; FastText, MultiLang Transformers, and Language-Specific Transformers to find the best working model for the classification challenge. We experimented with mentioned models and mostly reached the best prediction scores using the Language Specific Transformers. Our best-submitted result was ranked 3rd among submissions for the Amharic language, obtaining an F1 score of 0.702 behind the second-ranked system.",
}
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<abstract>This paper describes our contribution to SemEval-23 Shared Task 12: ArfiSenti. The task consists of several sentiment classification subtasks for rarely studied African languages to predict positive, negative, or neutral classes of a given Twitter dataset. In our system we utilized three different models; FastText, MultiLang Transformers, and Language-Specific Transformers to find the best working model for the classification challenge. We experimented with mentioned models and mostly reached the best prediction scores using the Language Specific Transformers. Our best-submitted result was ranked 3rd among submissions for the Amharic language, obtaining an F1 score of 0.702 behind the second-ranked system.</abstract>
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%0 Conference Proceedings
%T Sefamerve at SemEval-2023 Task 12: Semantic Evaluation of Rarely Studied Languages
%A Delil, Selman
%A Kuyumcu, Birol
%Y Ojha, Atul Kr.
%Y Doğruöz, A. Seza
%Y Da San Martino, Giovanni
%Y Tayyar Madabushi, Harish
%Y Kumar, Ritesh
%Y Sartori, Elisa
%S Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F delil-kuyumcu-2023-sefamerve
%X This paper describes our contribution to SemEval-23 Shared Task 12: ArfiSenti. The task consists of several sentiment classification subtasks for rarely studied African languages to predict positive, negative, or neutral classes of a given Twitter dataset. In our system we utilized three different models; FastText, MultiLang Transformers, and Language-Specific Transformers to find the best working model for the classification challenge. We experimented with mentioned models and mostly reached the best prediction scores using the Language Specific Transformers. Our best-submitted result was ranked 3rd among submissions for the Amharic language, obtaining an F1 score of 0.702 behind the second-ranked system.
%R 10.18653/v1/2023.semeval-1.71
%U https://aclanthology.org/2023.semeval-1.71
%U https://doi.org/10.18653/v1/2023.semeval-1.71
%P 512-516
Markdown (Informal)
[Sefamerve at SemEval-2023 Task 12: Semantic Evaluation of Rarely Studied Languages](https://aclanthology.org/2023.semeval-1.71) (Delil & Kuyumcu, SemEval 2023)
ACL