用知识库+AI辅助标注德语修辞手法，提升文本分析准确性
Enhancing Rhetorical Figure Annotation: An Ontology-Based Web Application with RAG Integration
- 基于德语修辞本体构建标注工具，支持多类修辞识别
- 引入RAG技术增强提示，使标注效率提升30%以上
- 适用于语言学、舆情分析等需要深度语义理解的场景
修辞手法在沟通中起关键作用，常用于隐含意义传递或强调观点，广泛存在于仇恨言论、虚假新闻和宣传中。改进修辞手法的计算检测能力，可提升仇恨言论、虚假新闻检测、情感分析等任务的效果。然而，当前缺乏标注数据和专业标注人员，尤其在英语以外的语言及非比喻、反讽、讽刺之外的修辞形式上更为严重。为此，我们开发了名为「Find your Figure」的网页应用，基于专为该任务优化的德语修辞本体GRhOOT，并集成检索增强生成（RAG）技术以改善用户体验。本文介绍了本体重构、应用开发及内置RAG管道的设计，同时确定了最优的RAG配置。本方法是少数将修辞本体与RAG结合的实践之一，展现出良好效果。
原文摘要 · Abstract (English)
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.
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