Rhetorical figures play an important role in our communication. They are used to convey subtle, implicit meaning, or to emphasize statements. We notice them in hate speech, fake news, and propaganda. By improving the systems for computational detection of rhetorical figures, we can also improve tasks such as hate speech and fake news detection, sentiment analysis, opinion mining, or argument mining. Unfortunately, there is a lack of annotated data, as well as qualified annotators that would help us build large corpora to train machine learning models for the detection of rhetorical figures. The situation is particularly difficult in languages other than English, and for rhetorical figures other than metaphor, sarcasm, and irony. To overcome this issue, we develop a web application called "Find your Figure" that facilitates the identification and annotation of German rhetorical figures. The application is based on the German Rhetorical ontology GRhOOT which we have specially adapted for this purpose. In addition, we improve the user experience with Retrieval Augmented Generation (RAG). In this paper, we present the restructuring of the ontology, the development of the web application, and the built-in RAG pipeline. We also identify the optimal RAG settings for our application. Our approach is one of the first to practically use rhetorical ontologies in combination with RAG and shows promising results.

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.

We argue, reason, cajole, and persuade -- we deploy the overtly purposive use of figures. The traditional literary rhetoric -- because we are social animals endowed with a purpose, generating aesthetic pleasure, is best known (poetry symbolic mode of thought and communication who seek and fiction, myths and prayers, songs and jokes, are highly to shape our social environment, to compete, and to cooperate. But mnemonic formulas, As rhetoricians, philosophers, and semiologists have proverbs, oral traditions, children's literature, marketing regularly noticed, some patterns of argumentation and cajolery - in short, any linguistic configuration serving purposes are more successful than others. These patterns of usage in which mental characteristics like attention, learnability, -- collectively known as rhetorical figures -- include both and recollection are at a premium - follows one syntactic and semantic patterns, but it is the schemes (e.g., or several grooves that rhetorical theorists in the classical alliteration (word-initial consonant repetition), assonance and early-modern periods identified with rhetorical figures. The repetition, incrementation, and the like), the insight that importance of rhetorical figuration in modelling aspects of motivates this project is unmistakeable. We believe incorporating rhetorical figuration rhetoric-based metrics for text summarization) into natural language systems will have profound implications.