A scalable framework for stylometric analysis of multi-author documents

14Citations
Citations of this article
11Readers
Mendeley users who have this article in their library.
Get full text

Abstract

Stylometry is a statistical technique used to analyze the variations in the author’s writing styles and is typically applied to authorship attribution problems. In this investigation, we apply stylometry to authorship identification of multi-author documents (AIMD) task. We propose an AIMD technique called Co-Authorship Graph (CAG) which can be used to collaboratively attribute different portions of documents to different authors belonging to the same community. Based on CAG, we propose a novel AIMD solution which (i) significantly outperforms the existing state-of-the-art solution; (ii) can effectively handle a larger number of co-authors; and (iii) is capable of handling the case when some of the listed co-authors have not contributed to the document as a writer. We conducted an extensive experimental study to compare the proposed solution and the best existing AIMD method using real and synthetic datasets. We show that the proposed solution significantly outperforms existing state-of-the-art method.

Cite

CITATION STYLE

APA

Sarwar, R., Yu, C., Nutanong, S., Urailertprasert, N., Vannaboot, N., & Rakthanmanon, T. (2018). A scalable framework for stylometric analysis of multi-author documents. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10827 LNCS, pp. 813–829). Springer Verlag. https://doi.org/10.1007/978-3-319-91452-7_52

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free