Grammar as a Behavioral Biometric: Using Cognitively Motivated Grammar Models for Authorship Verification

[2403.08462] Grammar as a Behavioral Biometric: Using Cognitively Motivated Grammar Models for Authorship Verification Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Computation and Language arXiv:2403.08462 (cs) [Submitted on 13 Mar 2024 ( v1 ), last revised 10 Apr 2026 (this version, v3)] Title: Grammar as a Behavioral Biometric: Using Cognitively Motivated Grammar Models for Authorship Verification Authors: Andrea Nini , Oren Halvani , Lukas Graner , Sophie Titze , Valerio Gherardi , Shunichi Ishihara View a PDF of the paper titled Grammar as a Behavioral Biometric: Using Cognitively Motivated Grammar Models for Authorship Verification, by Andrea Nini and 5 other authors View PDF HTML (experimental) Abstract: Authorship Verification (AV) is a key area of research in digital text forensics, which addresses the fundamental question of whether two texts were written by the same person. Numerous computational approaches have been proposed over the last two decades in an attempt to address this challenge. However, existing AV methods often suffer from high complexity, low explainability and especially from a lack of clear scientific justification. We propose a simpler method based on modeling the grammar of an author following Cognitive Linguistics principles. These models are used to calculate $\lambda_G$ (LambdaG): the ratio of the likelihoods of a document given the candidate's grammar versus given a reference population's grammar. Our empirical evaluation, conducted on twelve datasets and compared against seven baseline methods, demonstrates that LambdaG achieves superior performance, including against several neural network-based AV methods. LambdaG is also robust to small variations in the composition of the reference population and provides interpretable visualizations, enhancing its explainability. We argue that its effectiveness is due to the method's compatibility with Cognitive Linguistics theories predicting that a person's grammar is a behavioral biometric. Subjects: Computation and Language (cs.CL) ; Machine Learning (cs.LG) Cite as: arXiv:2403.08462 [cs.CL] (or arXiv:2403.08462v3 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2403.08462 Focus to learn more arXiv-issued DOI via DataCite Journal reference: Humanit Soc Sci Commun 13, 455 (2026) Related DOI : https://doi.org/10.1057/s41599-025-06340-3 Focus to learn more DOI(s) linking to related resources Submission history From: Andrea Nini [ view email ] [v1] Wed, 13 Mar 2024 12:25:47 UTC (319 KB) [v2] Mon, 7 Apr 2025 11:12:57 UTC (791 KB) [v3] Fri, 10 Apr 2026 05:33:25 UTC (779 KB) Full-text links: Access Paper: View a PDF of the paper titled Grammar as a Behavioral Biometric: Using Cognitively Motivated Grammar Models for Authorship Verification, by Andrea Nini and 5 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.CL < prev | next > new | recent | 2024-03 Change to browse by: cs cs.LG References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... 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