Recentred local profiles for authorship attribution

N-gram-based Author Profiles for Authorship Attribution

We present a novel method for computer-assisted authorship attribution based on characterlevel n-gram author profiles, which is motivated by an almost-forgotten, pioneering method in 1976. The existing approaches to automated authorship attribution implicitly build author profiles as vectors of feature weights, as language models, or similar. Our approach is based on byte-level n-grams, it is l...

Local Histograms of Character N-grams for Authorship Attribution

This paper proposes the use of local histograms (LH) over character n-grams for authorship attribution (AA). LHs are enriched histogram representations that preserve sequential information in documents; they have been successfully used for text categorization and document visualization using word histograms. In this work we explore the suitability of LHs over n-grams at the character-level for ...

Enhancing Authorship Attribution By Utilizing Syntax Tree Profiles

The aim of modern authorship attribution approaches is to analyze known authors and to assign authorships to previously unseen and unlabeled text documents based on various features. In this paper we present a novel feature to enhance current attribution methods by analyzing the grammar of authors. To extract the feature, a syntax tree of each sentence of a document is calculated, which is then...

Authorship Attribution

Authorship attribution, 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 a wide range of application. Recent work in “non-traditional” authorship attribution demonstrates the practicality of automatically analyzing documents based on authorial style, but the state of the art is confusing. An...

Authorship Attribution for Electronic Documents