Enhancing Rhetorical Figure Annotation: An Ontology-Based Web Application with RAG Integration

TL;DR

This paper introduces a web application that leverages an adapted German rhetorical ontology and Retrieval Augmented Generation (RAG) to facilitate the annotation of rhetorical figures, aiding in NLP tasks like hate speech detection.

Contribution

It presents a novel integration of rhetorical ontologies with RAG in a web tool for annotating German rhetorical figures, addressing data scarcity in non-English languages.

Findings

The web application improves annotation efficiency.

Optimal RAG settings enhance figure identification.

Promising results in using ontologies with RAG for rhetorical analysis.

Abstract

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.