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Authorship identification ascertains the authorship of texts whose originsremain undisclosed. That authorship identification techniques work as reliablyas they do has been attributed to the fact that authorial style is properlycaptured and represented. Although modern authorship identification methodshave evolved significantly over the years and have proven effective indistinguishing authorial styles, the generalization of stylistic featuresacross domains has not been systematically reviewed. The presented workaddresses the challenge of enhancing the generalization of stylisticrepresentations in authorship identification, particularly when there arediscrepancies between training and testing samples. A comprehensive review ofempirical studies was conducted, focusing on various stylistic features andtheir effectiveness in representing an author's style. The influencing factorssuch as topic, genre, and register on writing style were also explored, alongwith strategies to mitigate their impact. While some stylistic features, likecharacter n-grams and function words, have proven to be robust anddiscriminative, others, such as content words, can introduce biases and hindercross-domain generalization. Representations learned using deep learningmodels, especially those incorporating character n-grams and syntacticinformation, show promise in enhancing representation generalization. Thefindings underscore the importance of selecting appropriate stylistic featuresfor authorship identification, especially in cross-domain scenarios. Therecognition of the strengths and weaknesses of various linguistic featurespaves the way for more accurate authorship identification in diverse contexts.

Enhancing Representation Generalization in Authorship Identification