Work

Comparison of Grammar Characteristics of Human-Written Corpora and Machine-Generated Texts Using a Novel Rule-Based Parser

Year: 2025

Type: article

Abstract: As the prevalence of machine-written texts grows, it has become increasingly important to distinguish between human- and machine-generated content, especially when such texts are not explicitly labeled. Current artificial intelligence (AI) detection methods primarily focus on human-like characteristics, such as emotionality and subjectivity. However, these features can be easily modified through AI humanization, which involves altering word choice. In contrast, altering the underlying grammar without affecting the conveyed information is considerably more challenging. Thus, the grammatical characteristics of a text can be used as additional indicators of its origin. To address this, we employ a newly developed rule-based parser to analyze the grammatical structures in human- and machine-written texts. Our findings reveal systematic grammatical differences between human- and machine-written texts, providing a reliable criterion for the determination of the text origin. We further examine the stability of this criterion in the context of AI humanization and translation to other languages.

Source: Information

DOI: 10.3390/info16040274

Authors: Simon Strübbe, Irina Sidorenko, Renée Lampe

Institutions: TUM Klinikum, Technical University of Munich

Language: English

Topic: Authorship Attribution and Profiling

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $1,930

Funder: Stiftung Würth