Inference of Fine-grained Attributes of Bengali Corpus for Stylometry Detection
This work addresses authorship identification and related tasks for Bengali text, but it is incremental as it applies existing methods to a new language.
The authors tackled the problem of stylometry detection for Bengali documents by using fine-grained attribute features and lexical markers with semi-supervised measures and majority voting, achieving reasonably promising accuracy compared to a baseline model.
Stylometry, the science of inferring characteristics of the author from the characteristics of documents written by that author, is a problem with a long history and belongs to the core task of Text categorization that involves authorship identification, plagiarism detection, forensic investigation, computer security, copyright and estate disputes etc. In this work, we present a strategy for stylometry detection of documents written in Bengali. We adopt a set of fine-grained attribute features with a set of lexical markers for the analysis of the text and use three semi-supervised measures for making decisions. Finally, a majority voting approach has been taken for final classification. The system is fully automatic and language-independent. Evaluation results of our attempt for Bengali author's stylometry detection show reasonably promising accuracy in comparison to the baseline model.