Authorship Verification based on the Likelihood Ratio of Grammar Models
Andrea Nini Oren Halvani Lukas Graner Valerio Gherardi Shunichi Ishihara
Authorship Verification (AV) is the process of analyzing a set of documents
to determine whether they were written by a specific author. This problem often
arises in forensic scenarios, e.g., in cases where the documents in question
constitute evidence for a crime. Existing state-of-the-art AV methods use
computational solutions that are not supported by a plausible scientific
explanation for their functioning and that are often difficult for analysts to
interpret. To address this, we propose a method relying on calculating a
quantity we call $\lambda_G$ (LambdaG): the ratio between the likelihood of a
document given a model of the Grammar for the candidate author and the
likelihood of the same document given a model of the Grammar for a reference
population. These Grammar Models are estimated using $n$-gram language models
that are trained solely on grammatical features. Despite not needing large
amounts of data for training, LambdaG still outperforms other established AV
methods with higher computational complexity, including a fine-tuned Siamese
Transformer network. Our empirical evaluation based on four baseline methods
applied to twelve datasets shows that LambdaG leads to better results in terms
of both accuracy and AUC in eleven cases and in all twelve cases if considering
only topic-agnostic methods. The algorithm is also highly robust to important
variations in the genre of the reference population in many cross-genre
comparisons. In addition to these properties, we demonstrate how LambdaG is
easier to interpret than the current state-of-the-art. We argue that the
advantage of LambdaG over other methods is due to fact that it is compatible
with Cognitive Linguistic theories of language processing.