SubmissionNumber#=%=#154
FinalPaperTitle#=%=#Tübingen-CL at SemEval-2024 Task 1: Ensemble Learning for Semantic Relatedness Estimation
ShortPaperTitle#=%=#
NumberOfPages#=%=#7
CopyrightSigned#=%=#Leixin Zhang
JobTitle#==#
Organization#==#
Abstract#==#The paper introduces our system for SemEval-2024 Task 1, which aims to predict the relatedness of sentence pairs. Operating under the hypothesis that semantic relatedness is a broader concept that extends beyond mere similarity of sentences, our approach seeks to identify useful features for relatedness estimation. We employ an ensemble approach integrating various systems, including statistical textual features and outputs of deep learning models to predict relatedness scores. The findings suggest that semantic relatedness can be inferred from various sources and ensemble models outperform many individual systems in estimating semantic relatedness.
Author{1}{Firstname}#=%=#Leixin
Author{1}{Lastname}#=%=#Zhang
Author{1}{Username}#=%=#leixinzhang
Author{1}{Email}#=%=#lzhang1@tcd.ie
Author{1}{Affiliation}#=%=#Universität Tübingen
Author{2}{Firstname}#=%=#Çağrı
Author{2}{Lastname}#=%=#Çöltekin
Author{2}{Username}#=%=#cagri
Author{2}{Email}#=%=#ccoltekin@sfs.uni-tuebingen.de
Author{2}{Affiliation}#=%=#University of Tübingen

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