An Ensemble Learning Based Author Identification System

Abstract

Author identification is an emerging domain in the area of Natural Language Processing (NLP) that allows us to identify the respective author of a particular piece of text. Every author had some unique characteristics of writing that involves their signature style of applying specific terms, making their piece of art distinct and noticeable and also there exists an extended story behind the linguistic and stylistic analysis in the identification of authors. In this paper we aim to produce a content resemblance based author identification system (AIS) using ensemble learning model for identification of author for a given piece of text. The experiment was tested on approximately 6,000 passages from 26 authors obtained from Bangla literature. Experimental results reported that the proposed technique performed better compare to the state-of-the art methods.