Ron Keinan
2024
Text Mining at SemEval-2024 Task 1: Evaluating Semantic Textual Relatedness in Low-resource Languages using Various Embedding Methods and Machine Learning Regression Models
Ron Keinan
Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)
In this paper, I describe my submission to the SemEval-2024 contest. I tackled subtask 1 - “Semantic Textual Relatedness for African and Asian Languages”. To find the semantic relatedness of sentence pairs, I tackled this task by creating models for nine different languages. I then vectorized the text data using a variety of embedding techniques including doc2vec, tf-idf, Sentence-Transformers, Bert, Roberta, and more, and used 11 traditional machine learning techniques of the regression type for analysis and evaluation.
2023
JCT at SemEval-2023 Tasks 12 A and 12B: Sentiment Analysis for Tweets Written in Low-resource African Languages using Various Machine Learning and Deep Learning Methods, Resampling, and HyperParameter Tuning
Ron Keinan
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Yaakov Hacohen-Kerner
Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)
In this paper, we describe our submissions to the SemEval-2023 contest. We tackled subtask 12 - “AfriSenti-SemEval: Sentiment Analysis for Low-resource African Languages using Twitter Dataset”. We developed different models for 12 African languages and a 13th model for a multilingual dataset built from these 12 languages. We applied a wide variety of word and char n-grams based on their tf-idf values, 4 classical machine learning methods, 2 deep learning methods, and 3 oversampling methods. We used 12 sentiment lexicons and applied extensive hyperparameter tuning.
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