Intrinsic Author Verification Using Topic Modeling
Nektaria Potha, Efstathios Stamatatos · 2018
Author verification is a fundamental task in authorship analysis and associated with important applications in humanities and forensics. In this paper, we propose the use of an intrinsic profile-based verification method that is based on latent semantic indexing (LSI). Our proposed approach is easy-to-follow and language independent. Based on experiments using benchmark corpora from the PAN shared task in author verification, we demonstrate that LSI is both more effective and more stable than latent Dirichlet allocation in this task. Moreover, LSI models are able to outperform existing approaches especially when multiple texts of known authorship are available per verification instance and all documents belong to the same thematic area and genre. We also study several feature types and similarity measures to be combined with the proposed topic models.