SubmissionNumber#=%=#291
FinalPaperTitle#=%=#Groningen Team A at SemEval-2024 Task 8: Human/Machine Authorship Attribution Using a Combination of Probabilistic and Linguistic Features
ShortPaperTitle#=%=#
NumberOfPages#=%=#7
CopyrightSigned#=%=#Alon Scheuer
JobTitle#==#
Organization#==#
Abstract#==#Our approach primarily centers on feature-based systems, where a diverse array of features pertinent to the text's linguistic attributes is extracted. Alongside those, we incorporate token-level probabilistic features which are fed into a Bidirectional Long Short-Term Memory (BiLSTM) model. Both resulting feature arrays are concatenated and fed into our final prediction model. Our method under-performed compared to the baseline, despite the fact that previous attempts by others have successfully used linguistic features for the purpose of discerning machine-generated text. We conclude that our examined subset of linguistically motivated features alongside probabilistic features was not able to contribute almost any performance at all to a hybrid classifier of human and machine texts.
Author{1}{Firstname}#=%=#Huseyin
Author{1}{Lastname}#=%=#Alecakir
Author{1}{Email}#=%=#huseyinalecakir@gmail.com
Author{1}{Affiliation}#=%=#University of Groningen
Author{2}{Firstname}#=%=#Puja
Author{2}{Lastname}#=%=#Chakraborty
Author{2}{Email}#=%=#p.chakraborty.2@student.rug.nl
Author{2}{Affiliation}#=%=#University of Groningen
Author{3}{Firstname}#=%=#Pontus
Author{3}{Lastname}#=%=#Henningsson
Author{3}{Email}#=%=#p.p.i.henningsson@student.rug.nl
Author{3}{Affiliation}#=%=#University of Groningen
Author{4}{Firstname}#=%=#Matthijs
Author{4}{Lastname}#=%=#van Hofslot
Author{4}{Email}#=%=#m.van.hofslot@student.rug.nl
Author{4}{Affiliation}#=%=#University of Groningen
Author{5}{Firstname}#=%=#Alon
Author{5}{Lastname}#=%=#Scheuer
Author{5}{Email}#=%=#a.scheuer.1@student.rug.nl
Author{5}{Affiliation}#=%=#University of Groningen

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