@inproceedings{reyes-magana-etal-2025-tilen,
title = "{TIL}e{N} at {S}em{E}val-2025 Task 11: A Transformer-Based Model for Sentiment Classification Applied to the {R}ussian Language",
author = "Reyes - Maga{\~n}a, Jorge and
Basto - D{\'i}az, Luis and
Curi - Quintal, Luis Fernando",
editor = "Rosenthal, Sara and
Ros{\'a}, Aiala and
Ghosh, Debanjan and
Zampieri, Marcos",
booktitle = "Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.semeval-1.265/",
pages = "2044--2049",
ISBN = "979-8-89176-273-2",
abstract = "We present our approach to tackle the Sentiment Classification Task. The task was divided into 3 categories: 1) Track A: Multi-label Emotion Detection 2) Track B: Emotion Intensity, and 3) Cross-lingual Emotion Detection. We participate in subtasks 1 and 2 for the Russian language. Our main approach is summarized as using pre-trained language models and afterwords working with fine-tuning aside the corpora provided. During the development phase, we had promising outcomes. Later during the test phase, we got similar scores to the Semeval baseline. Our approach is easy to replicate and we proportionate every detail of the process performed."
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<abstract>We present our approach to tackle the Sentiment Classification Task. The task was divided into 3 categories: 1) Track A: Multi-label Emotion Detection 2) Track B: Emotion Intensity, and 3) Cross-lingual Emotion Detection. We participate in subtasks 1 and 2 for the Russian language. Our main approach is summarized as using pre-trained language models and afterwords working with fine-tuning aside the corpora provided. During the development phase, we had promising outcomes. Later during the test phase, we got similar scores to the Semeval baseline. Our approach is easy to replicate and we proportionate every detail of the process performed.</abstract>
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%0 Conference Proceedings
%T TILeN at SemEval-2025 Task 11: A Transformer-Based Model for Sentiment Classification Applied to the Russian Language
%A Reyes - Magaña, Jorge
%A Basto - Díaz, Luis
%A Curi - Quintal, Luis Fernando
%Y Rosenthal, Sara
%Y Rosá, Aiala
%Y Ghosh, Debanjan
%Y Zampieri, Marcos
%S Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-273-2
%F reyes-magana-etal-2025-tilen
%X We present our approach to tackle the Sentiment Classification Task. The task was divided into 3 categories: 1) Track A: Multi-label Emotion Detection 2) Track B: Emotion Intensity, and 3) Cross-lingual Emotion Detection. We participate in subtasks 1 and 2 for the Russian language. Our main approach is summarized as using pre-trained language models and afterwords working with fine-tuning aside the corpora provided. During the development phase, we had promising outcomes. Later during the test phase, we got similar scores to the Semeval baseline. Our approach is easy to replicate and we proportionate every detail of the process performed.
%U https://aclanthology.org/2025.semeval-1.265/
%P 2044-2049
Markdown (Informal)
[TILeN at SemEval-2025 Task 11: A Transformer-Based Model for Sentiment Classification Applied to the Russian Language](https://aclanthology.org/2025.semeval-1.265/) (Reyes - Magaña et al., SemEval 2025)
ACL