SubmissionNumber#=%=#16
FinalPaperTitle#=%=#SATLab at SemEval-2024 Task 1: A Fully Instance-Specific Approach for Semantic Textual Relatedness Prediction
ShortPaperTitle#=%=#
NumberOfPages#=%=#6
CopyrightSigned#=%=#Bestgen
JobTitle#==#
Organization#==#Université catholique de Louvain, Place de l'Université, 1, B 1348 Louvain-la-Neuve, Belgium
Abstract#==#This paper presents the SATLab participation in SemEval 2024 Task 1 on Semantic Textual Relatedness. The proposed system predicts semantic relatedness by means of the Euclidean distance between the character ngram frequencies in the two sentences to evaluate. It employs no external resources, nor information from other instances present in the material. The system performs well, coming first in five of the twelve languages. However, there is little difference between the best systems.
Author{1}{Firstname}#=%=#Yves
Author{1}{Lastname}#=%=#Bestgen
Author{1}{Username}#=%=#ybestgen
Author{1}{Email}#=%=#yves.bestgen@uclouvain.be
Author{1}{Affiliation}#=%=#Université catholique de Louvain

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