Computational stylistics in the age of artificial intelligence: A linguistic perspective
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Abstract
The swift development of artificial intelligence has led to a revolution within the realm of stylistics, thus introducing the new interdisciplinary approach known as computational stylistics. This research paper discusses the current state of affairs when it comes to using AI-based methods for stylistic analysis, namely how approaches used in Computational Linguistics and NLP (Natural Language Processing) help to further advance the field. Rather than relying solely on rule-based and purely qualitative means of analysis, this study takes a step toward data-driven methods based on machine learning models, including both deep learning networks and transformer-based architectures.
Based on experimental results, the current study seeks to explore whether various computational models can be successfully applied to stylistics and help identify such stylistic markers as lexis, syntax, and semantics within texts. It turns out that the use of contextual representation models helps to achieve better results when it comes to recognizing and classifying stylistic phenomena. In addition, large-scale pretraining becomes another critical factor in this type of stylistics research.
However, in addition to its achievements, the work also highlights important problems, such as issues related to model interpretability, biases within the training dataset, and the underrepresentation of different styles of speech. In terms of the theoretical approach, the study claims that the use of artificial intelligence is not intended to supersede traditional stylistic approaches, but rather complements them.
In conclusion, this paper can be considered a contribution to the increasing amount of literature on computational stylistics, which demonstrates how artificial intelligence transforms the study of language style and underlines the importance of combining computational approaches and stylistics in an equal measure.
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