Authorship Attribution through Function Word Adjacency Networks

TL;DR

This paper introduces a novel authorship attribution method using function word adjacency networks (WANs), which model stylistic patterns based on grammatical function word relationships, outperforming frequency-based methods and benefiting from combined approaches.

Contribution

The paper presents a new stylometric technique using WANs to capture authorial style, demonstrating improved attribution accuracy and analyzing optimal parameters for this approach.

Findings

WANs outperform frequency-based methods in attribution accuracy

Combining WANs with word frequency methods increases accuracy

Function words provide content-independent stylistic signatures

Abstract

A method for authorship attribution based on function word adjacency networks (WANs) is introduced. Function words are parts of speech that express grammatical relationships between other words but do not carry lexical meaning on their own. In the WANs in this paper, nodes are function words and directed edges stand in for the likelihood of finding the sink word in the ordered vicinity of the source word. WANs of different authors can be interpreted as transition probabilities of a Markov chain and are therefore compared in terms of their relative entropies. Optimal selection of WAN parameters is studied and attribution accuracy is benchmarked across a diverse pool of authors and varying text lengths. This analysis shows that, since function words are independent of content, their use tends to be specific to an author and that the relational data captured by function WANs is a good summary of stylometric fingerprints. Attribution accuracy is observed to exceed the one achieved by methods that rely on word frequencies alone. Further combining WANs with methods that rely on word frequencies alone, results in larger attribution accuracy, indicating that both sources of information encode different aspects of authorial styles.