Enhancing Rhetorical Figure Annotation: An Ontology-Based Web Application with RAG Integration
This work addresses a data bottleneck for computational linguistics tasks such as hate speech and fake news detection, particularly for non-English languages, though it is incremental in applying existing RAG methods to a new domain.
The paper tackles the lack of annotated data for detecting rhetorical figures in languages like German by developing a web application called 'Find your Figure' that integrates a German rhetorical ontology with Retrieval Augmented Generation (RAG), showing promising results as one of the first practical uses of this combination.
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.