Document Provenance and Authentication through Authorship Classification
This work addresses document provenance and authentication, which is important for applications like collaborative writing and security, but it is incremental as it builds on existing text classification methods.
The paper tackles the problem of classifying single and multi-authored documents for style analysis, proposing an ensemble-based text-processing framework that incorporates various state-of-the-art algorithms and achieves significant performance improvements on a large-scale benchmark dataset.
Style analysis, which is relatively a less explored topic, enables several interesting applications. For instance, it allows authors to adjust their writing style to produce a more coherent document in collaboration. Similarly, style analysis can also be used for document provenance and authentication as a primary step. In this paper, we propose an ensemble-based text-processing framework for the classification of single and multi-authored documents, which is one of the key tasks in style analysis. The proposed framework incorporates several state-of-the-art text classification algorithms including classical Machine Learning (ML) algorithms, transformers, and deep learning algorithms both individually and in merit-based late fusion. For the merit-based late fusion, we employed several weight optimization and selection methods to assign merit-based weights to the individual text classification algorithms. We also analyze the impact of the characters on the task that are usually excluded in NLP applications during pre-processing by conducting experiments on both clean and un-clean data. The proposed framework is evaluated on a large-scale benchmark dataset, significantly improving performance over the existing solutions.