Computational Approaches to the Detection of Lesser-Known Rhetorical Figures: A Systematic Survey and Research Challenges

TL;DR

This paper systematically surveys computational methods for detecting lesser-known rhetorical figures, highlighting their importance in NLP and discussing challenges like data scarcity and language limitations.

Contribution

It provides a comprehensive overview of existing approaches, datasets, and challenges in computational detection of lesser-known rhetorical figures.

Findings

Identified key challenges such as dataset scarcity and language limitations.

Reviewed various detection approaches and their effectiveness.

Highlighted the need for more diverse datasets and advanced methods.

Abstract

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.