  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.
