@inproceedings{moctezuma-etal-2024-ingeotec,
title = "{INGEOTEC} at {S}em{E}val-2024 Task 10: Bag of Words Classifiers",
author = "Moctezuma, Daniela and
Tellez, Eric and
Ortiz Bejar, Jose and
Paredes, Mireya",
editor = {Ojha, Atul Kr. and
Do{\u{g}}ru{\"o}z, A. Seza and
Tayyar Madabushi, Harish and
Da San Martino, Giovanni and
Rosenthal, Sara and
Ros{\'a}, Aiala},
booktitle = "Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)",
month = jun,
year = "2024",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.semeval-1.162",
doi = "10.18653/v1/2024.semeval-1.162",
pages = "1115--1120",
abstract = "The Emotion Recognition in Conversation subtask aims to predict the emotions of the utterance of a conversation. In its most basic form, one can treat each utterance separately without considering that it is part of a conversation. Using this simplification, one can use any text classification algorithm to tackle this problem. This contribution follows this approach by solving the problem with different text classifiers based on Bag of Words. Nonetheless, the best approach takes advantage of the dynamics of the conversation; however, this algorithm is not statistically different than a Bag of Words with a Linear Support Vector Machine.",
}
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<abstract>The Emotion Recognition in Conversation subtask aims to predict the emotions of the utterance of a conversation. In its most basic form, one can treat each utterance separately without considering that it is part of a conversation. Using this simplification, one can use any text classification algorithm to tackle this problem. This contribution follows this approach by solving the problem with different text classifiers based on Bag of Words. Nonetheless, the best approach takes advantage of the dynamics of the conversation; however, this algorithm is not statistically different than a Bag of Words with a Linear Support Vector Machine.</abstract>
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%0 Conference Proceedings
%T INGEOTEC at SemEval-2024 Task 10: Bag of Words Classifiers
%A Moctezuma, Daniela
%A Tellez, Eric
%A Ortiz Bejar, Jose
%A Paredes, Mireya
%Y Ojha, Atul Kr.
%Y Doğruöz, A. Seza
%Y Tayyar Madabushi, Harish
%Y Da San Martino, Giovanni
%Y Rosenthal, Sara
%Y Rosá, Aiala
%S Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F moctezuma-etal-2024-ingeotec
%X The Emotion Recognition in Conversation subtask aims to predict the emotions of the utterance of a conversation. In its most basic form, one can treat each utterance separately without considering that it is part of a conversation. Using this simplification, one can use any text classification algorithm to tackle this problem. This contribution follows this approach by solving the problem with different text classifiers based on Bag of Words. Nonetheless, the best approach takes advantage of the dynamics of the conversation; however, this algorithm is not statistically different than a Bag of Words with a Linear Support Vector Machine.
%R 10.18653/v1/2024.semeval-1.162
%U https://aclanthology.org/2024.semeval-1.162
%U https://doi.org/10.18653/v1/2024.semeval-1.162
%P 1115-1120
Markdown (Informal)
[INGEOTEC at SemEval-2024 Task 10: Bag of Words Classifiers](https://aclanthology.org/2024.semeval-1.162) (Moctezuma et al., SemEval 2024)
ACL
- Daniela Moctezuma, Eric Tellez, Jose Ortiz Bejar, and Mireya Paredes. 2024. INGEOTEC at SemEval-2024 Task 10: Bag of Words Classifiers. In Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024), pages 1115–1120, Mexico City, Mexico. Association for Computational Linguistics.