Eric Tellez


2024

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INGEOTEC at SemEval-2024 Task 10: Bag of Words Classifiers
Daniela Moctezuma | Eric Tellez | Jose Ortiz Bejar | Mireya Paredes
Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)

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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INGEOTEC at SemEval-2024 Task 1: Bag of Words and Transformers
Daniela Moctezuma | Eric Tellez | Mario Graff
Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)

Understanding the meaning of a written message is crucial in solving problems related to Natural Language Processing; the relatedness of two or more messages is a semantic problem tackled with supervised and unsupervised learning. This paper outlines our submissions to the Semantic Textual Relatedness (STR) challenge at SemEval 2024, which is devoted to evaluating the degree of semantic similarity and relatedness between two sentences across multiple languages. We use two main strategies in our submissions. The first approach is based on the Bag-of-Word scheme, while the second one uses pre-trained Transformers for text representation. We found some attractive results, especially in cases where different models adjust better to certain languages over others.

2019

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INGEOTEC at SemEval-2019 Task 5 and Task 6: A Genetic Programming Approach for Text Classification
Mario Graff | Sabino Miranda-Jiménez | Eric Tellez | Daniela Alejandra Ochoa
Proceedings of the 13th International Workshop on Semantic Evaluation

This paper describes our participation in HatEval and OffensEval challenges for English and Spanish languages. We used several approaches, B4MSA, FastText, and EvoMSA. Best results were achieved with EvoMSA, which is a multilingual and domain-independent architecture that combines the prediction of different knowledge sources to solve text classification problems.