Computational Approaches to the Detection of Lesser-Known Rhetorical Figures: A Systematic Survey and Research Challenges
This work addresses the problem of improving text understanding in Natural Language Processing by surveying detection methods for rhetorical figures, but it is incremental as it reviews existing research without introducing new results.
The paper provides a systematic survey of computational approaches for detecting lesser-known rhetorical figures, identifying key challenges like dataset scarcity and reliance on rule-based methods.
Rhetorical figures play a major role in our everyday communication as they make text more interesting, more memorable, or more persuasive. Therefore, it is important to computationally detect rhetorical figures to fully understand the meaning of a text. We provide a comprehensive overview of computational approaches to lesser-known rhetorical figures. We explore the linguistic and computational perspectives on rhetorical figures, emphasizing their significance for the domain of Natural Language Processing. We present different figures in detail, delving into datasets, definitions, rhetorical functions, and detection approaches. We identified challenges such as dataset scarcity, language limitations, and reliance on rule-based methods.