An empirical framework to identify authorship from bengali literary works

Abstract

Authorship attribution is the process of identifying the probable author of an unknown document. This paper proposes a neural network based framework, which identifies the authorship from Bengali literary documents. For this purpose, a corpus consisting of 12,142 text documents of 23 writers/bloggers is built. A static dictionary is used to count vectorization and important features are selected using information gain. The proposed system is trained with 9099 documents and tested with 3043 documents. The experimental result shows that neural network with n-gram and parts of speech (PoS) features achieved 94% accuracy on developed corpus.