Author Verification Using Rich Set of Linguistic Features

Abstract

Author Verification is a type of author identification task, which deals with identification of whether two documents were written by the same author or not. Mainly, the detection performance depends on the used feature set for clustering the documents. Linguistic features have been utilized for author identification according to the writing style of a particular author. Disclosing the shallow changes of the author’s writing style is the major problem which should be addressed in the domain of authorship verification. It motivates the computer science researchers to do research on authorship verification in the field of computer forensics. In this work, three types of linguistic features such as stylistic, syntactic, and semantic features are used to improve the accuracy of author verification. The Naïve Bayes multinomial classifier is used to build the classification model and good accuracy is achieved for Author Verification.