Enhancing Representation Generalization in Authorship Identification
Haining Wang
Authorship identification ascertains the authorship of texts whose origins
remain undisclosed. That authorship identification techniques work as reliably
as they do has been attributed to the fact that authorial style is properly
captured and represented. Although modern authorship identification methods
have evolved significantly over the years and have proven effective in
distinguishing authorial styles, the generalization of stylistic features
across domains has not been systematically reviewed. The presented work
addresses the challenge of enhancing the generalization of stylistic
representations in authorship identification, particularly when there are
discrepancies between training and testing samples. A comprehensive review of
empirical studies was conducted, focusing on various stylistic features and
their effectiveness in representing an author's style. The influencing factors
such as topic, genre, and register on writing style were also explored, along
with strategies to mitigate their impact. While some stylistic features, like
character n-grams and function words, have proven to be robust and
discriminative, others, such as content words, can introduce biases and hinder
cross-domain generalization. Representations learned using deep learning
models, especially those incorporating character n-grams and syntactic
information, show promise in enhancing representation generalization. The
findings underscore the importance of selecting appropriate stylistic features
for authorship identification, especially in cross-domain scenarios. The
recognition of the strengths and weaknesses of various linguistic features
paves the way for more accurate authorship identification in diverse contexts.