Authorship Verification Method Based on Grammar Models

Conceptos Básicos

LambdaG, a method based on the likelihood ratio of grammar models, outperforms other AV methods in accuracy and AUC.

Resumen

The article introduces LambdaG, a novel authorship verification method based on the likelihood ratio of grammar models. Existing AV methods lack scientific explanations and are hard to interpret. LambdaG calculates the likelihood ratio between a document given a model of the author's grammar and a model from a reference population. It outperforms established AV methods with higher computational complexity. The method is robust across different genres and easier to interpret than current state-of-the-art techniques. LambdaG aligns with Cognitive Linguistic theories of language processing.

Estadísticas

LambdaG leads to better results in terms of accuracy and AUC in eleven cases.
The algorithm is highly robust to variations in the genre of the reference population.
LambdaG outperforms other AV methods with higher computational complexity.

Citas

Ideas clave extraídas de

Authorship Verification based on the Likelihood Ratio of Grammar Models

by Andrea Nini,... a las arxiv.org 03-14-2024

https://arxiv.org/pdf/2403.08462.pdf

Consultas más profundas

How does LambdaG address the issue of content bias in authorship verification?

LambdaG addresses the issue of content bias in authorship verification by focusing on grammatical features rather than content-specific information. By utilizing n-gram language models trained solely on grammatical tokens, LambdaG ensures that the analysis is based on linguistic patterns and structures rather than topic-related words. This approach helps to mitigate the influence of content variations across different genres or topics, which can often lead to misleading results in traditional authorship verification methods.
Additionally, LambdaG incorporates a pre-processing step called POSNoise, which replaces non-functional words with their Part-Of-Speech tags. This technique further reduces the impact of content bias by emphasizing functional elements over specific vocabulary. By prioritizing grammatical features and minimizing reliance on topic-specific terms, LambdaG provides a more robust and reliable method for authorship verification that is less susceptible to variations in writing style or subject matter.

What implications does LambdaG have for forensic text comparison in legal settings?

LambdaG offers significant implications for forensic text comparison in legal settings due to its ability to provide a scientifically sound and interpretable method for authorship verification. The likelihood ratio framework used by LambdaG aligns with principles of forensic science, allowing analysts to quantify the strength of evidence supporting or refuting authorship claims objectively.
In legal contexts where questioned documents serve as crucial evidence, such as criminal investigations or civil disputes, LambdaG's robustness against content bias and genre variations enhances the reliability of findings. The method's focus on grammar models derived from known authors' writing styles enables analysts to make informed decisions about document authenticity based on linguistic patterns rather than superficial textual characteristics.
Moreover, LambdaG's calibration into a log-likelihood ratio through logistic regression provides a standardized measure of evidential strength that can be effectively communicated in court proceedings. This not only enhances the credibility of authorship verification outcomes but also facilitates clearer explanations for judges and juries regarding the basis for conclusions drawn from linguistic analysis.

How can LambdaG be applied to real-world scenarios beyond academic research?

LambdaG has practical applications beyond academic research in various real-world scenarios where determining document authenticity is critical. In journalism and media industries, it can be utilized to verify news articles' authorship or investigate cases involving ghostwriting or unauthorized publication. By analyzing writing styles based on grammatical features rather than topical content, LambdaG can assist in confirming authors' identities accurately.
In forensic linguistics and law enforcement agencies, LambdaG can play a vital role in analyzing ransom notes, blackmail letters, or other malicious texts used as evidence in criminal investigations. Its ability to address content bias issues makes it particularly valuable when dealing with diverse genres or topics within questioned documents.
Furthermore, organizations combating academic misconduct could benefit from using LambdaG to detect plagiarism or contract cheating among students by verifying originality based on linguistic patterns instead of specific wording choices. Overall,
Lambda G's versatility allows it to be applied effectively across multiple industries requiring accurate
authorship attribution techniques outside academia.