SubmissionNumber#=%=#63
FinalPaperTitle#=%=#Kathlalu at SemEval-2024 Task 8: A Comparative Analysis of Binary Classification Methods for Distinguishing Between Human and Machine-generated Text
ShortPaperTitle#=%=#
NumberOfPages#=%=#4
CopyrightSigned#=%=#Lujia Cao, Ece Lara Kilic, Katharina Will
JobTitle#==#student
Organization#==#University of Tübingen
Abstract#==#This paper investigates two methods for constructing a binary classifier to distinguish between human-generated and machine-generated text. The main emphasis is on a straightforward approach based on Zipf's law, which, despite its simplicity, achieves a moderate level of performance. Additionally, the paper briefly discusses experimentation with the utilization of unigram word counts.
Author{1}{Firstname}#=%=#Lujia
Author{1}{Lastname}#=%=#Cao
Author{1}{Email}#=%=#lujia.cao@student.uni-tuebingen.de
Author{1}{Affiliation}#=%=#University of Tübingen
Author{2}{Firstname}#=%=#Ece Lara
Author{2}{Lastname}#=%=#Kilic
Author{2}{Email}#=%=#ece-lara.kilic@student.uni-tuebingen.de
Author{2}{Affiliation}#=%=#University of Tübingen
Author{3}{Firstname}#=%=#Katharina
Author{3}{Lastname}#=%=#Will
Author{3}{Email}#=%=#katharina.will@student.uni-tuebingen.de
Author{3}{Affiliation}#=%=#University of Tübingen

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