SubmissionNumber#=%=#66
FinalPaperTitle#=%=#Text Mining at SemEval-2024 Task 1: Evaluating Semantic Textual Relatedness in Low-resource Languages using Various Embedding Methods and Machine Learning Regression Models
ShortPaperTitle#=%=#
NumberOfPages#=%=#12
CopyrightSigned#=%=#Ron Keinan
JobTitle#==#
Organization#==#
Abstract#==#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.
Author{1}{Firstname}#=%=#Ron
Author{1}{Lastname}#=%=#Keinan
Author{1}{Username}#=%=#ronke21
Author{1}{Email}#=%=#ronke21@gmail.com
Author{1}{Affiliation}#=%=#Jerusalem College of Technology, Lev Academic Center

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