Matthijs Van Hofslot


2024

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Groningen Team A at SemEval-2024 Task 8: Human/Machine Authorship Attribution Using a Combination of Probabilistic and Linguistic Features
Huseyin Alecakir | Puja Chakraborty | Pontus Henningsson | Matthijs Van Hofslot | Alon Scheuer
Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)

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.