DIGITAL INTERTEXTUALITY AND MACHINE LEARNING: AUTOMATED DETECTION OF LITERARY ALLUSIONS IN CONTEMPORARY ENGLISH FICTION
Keywords:
digital intertextuality, machine learning, literary allusion, contemporary English fiction, natural language processing, distant reading, semantic similarity, computational philology, text mining, digital humanities.Abstract
This article examines the role of machine learning in the automated detection of literary allusions in contemporary English fiction. In the digital age, intertextuality is no longer limited to the intuitive recognition of textual echoes by individual scholars but can also be explored through computational methods that identify lexical, semantic, and structural relationships between texts. The article discusses the possibilities and limitations of using natural language processing (NLP), semantic similarity models, and distant reading techniques to detect both explicit and implicit literary allusions. It argues that machine learning can assist researchers in processing large literary corpora, uncovering hidden intertextual patterns, and comparing contemporary fiction with earlier literary traditions. At the same time, the study emphasizes that automated detection cannot fully replace human interpretation, since literary allusions often depend on cultural memory, authorial intention, irony, and contextual nuance. The article concludes that the most effective approach is a hybrid model that combines computational analysis with traditional philological interpretation.
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