Computational detection of rhetorical figures focuses mostly on figures such as metaphor, irony, or analogy. However, there exist many more figures that are neither less important nor less prevalent. We wanted to pinpoint the reasons why researchers often avoid other figures and to shed light on the challenges they struggle with when investigating those figures. In this comprehensive survey, we analyzed over 40 papers dealing with the computational detection of rhetorical figures other than metaphor, simile, sarcasm, and irony. We encountered recurrent challenges from which we compiled a ten point list. Furthermore, we suggest solutions for each challenge to encourage researchers to investigate a greater variety of rhetorical figures.

Rhetorical figures play an important role in influencing readers and listeners. Some of these word constructs that deviate from the usual language structure are known to be persuasive – antithesis is one of them. This figure combines parallel phrases with opposite ideas or words to highlight a contradiction. By identifying this figure, persuasive actors can be better identified. For this task, we create an annotated German dataset for antithesis detection. The dataset consists of posts from a Telegram channel criticizing the COVID-19 politics in Germany. Furthermore, we propose a three-block pipeline approach to detect the figure antithesis using large language models. Our pipeline splits the text into phrases, identifies phrases with a syntactically parallel structure, and detects if these parallel phrase pairs present opposing ideas by fine-tuning the German ELECTRA model, a state-of-the-art deep learning model for the German language. Furthermore, we compare the results with multilingual BERT and German BERT. Our novel approach outperforms the state-of-the-art methods (F1-score of 50.43 %) for antithesis detection by achieving an F1-score of 65.11 %.

GRhOOT, the German RhetOrical OnTology, is a domain ontology of 110 rhetorical figures in the German language. The overall goal of building an ontology of rhetorical figures in German is not only the formal representation of different rhetorical figures, but also allowing for their easier detection, thus improving sentiment analysis, argument mining, detection of hate speech and fake news, machine translation, and many other tasks in which recognition of non-literal language plays an important role. The challenge of building such ontologies lies in classifying the figures and assigning adequate characteristics to group them, while considering their distinctive features. The ontology of rhetorical figures in the Serbian language was used as a basis for our work. Besides transferring and extending the concepts of the Serbian ontology, we ensured completeness and consistency by using description logic and SPARQL queries. Furthermore, we show a decision tree to identify figures and suggest a usage scenario on how the ontology can be utilized to collect and annotate data.