Using lexical stress in authorship attribution of historical texts

Abstract

This paper presents some early results from a comprehensive project, whose goal is to investigate the use of intonation and lexical stress in authorship attribution. We demonstrate how lexical stress patterns extracted from written text can be used to train a variety of machine learning algorithms to perform attribution of texts of unknown or disputed authorship. Specifically, we apply our methodology to a collection of 18th century American and British political writings, and demonstrate how combining lexical stress with other lexical features can significantly improve the attribution results.