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.- Anthology ID:
- 2024.semeval-1.62
- Volume:
- Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)
- Month:
- June
- Year:
- 2024
- Address:
- Mexico City, Mexico
- Editors:
- Atul Kr. Ojha, A. Seza Doğruöz, Harish Tayyar Madabushi, Giovanni Da San Martino, Sara Rosenthal, Aiala Rosá
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 399–402
- Language:
- URL:
- https://aclanthology.org/2024.semeval-1.62
- DOI:
- Cite (ACL):
- Lujia Cao, Ece Lara Kilic, and Katharina Will. 2024. Kathlalu at SemEval-2024 Task 8: A Comparative Analysis of Binary Classification Methods for Distinguishing Between Human and Machine-generated Text. In Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024), pages 399–402, Mexico City, Mexico. Association for Computational Linguistics.
- Cite (Informal):
- Kathlalu at SemEval-2024 Task 8: A Comparative Analysis of Binary Classification Methods for Distinguishing Between Human and Machine-generated Text (Cao et al., SemEval 2024)
- PDF:
- https://preview.aclanthology.org/jeptaln-2024-ingestion/2024.semeval-1.62.pdf