用INCEpTION标注体育新闻中的修辞关系，对比人工与大模型效果。
Annotate Rhetorical Relations with INCEpTION: A Comparison with Automatic Approaches
- 用INCEpTION工具手动标注板球新闻的修辞关系
- DistilBERT在分类任务中准确率最高
- 适合研究话语分析与Transformer模型的应用
本研究探讨使用INCEpTION工具对话语中的修辞关系进行标注，并比较人工标注与基于大语言模型的自动方法。研究聚焦于体育报道（特别是板球新闻），评估BERT、DistilBERT和逻辑回归模型在分类修辞关系（如阐释、对比、背景、因果）上的表现。结果表明，DistilBERT取得了最高准确率，突显其在高效话语关系预测中的潜力。该工作推动了话语解析与基于Transformer的自然语言处理的交叉发展。
原文摘要 · Abstract (English)
This research explores the annotation of rhetorical relations in discourse using the INCEpTION tool and compares manual annotation with automatic approaches based on large language models. The study focuses on sports reports (specifically cricket news) and evaluates the performance of BERT, DistilBERT, and Logistic Regression models in classifying rhetorical relations such as elaboration, contrast, background, and cause-effect. The results show that DistilBERT achieved the highest accuracy, highlighting its potential for efficient discourse relation prediction. This work contributes to the growing intersection of discourse parsing and transformer-based NLP. (This paper was conducted as part of an academic requirement under the supervision of Prof. Dr. Ralf Klabunde, Linguistic Data Science Lab, Ruhr University Bochum.) Keywords: Rhetorical Structure Theory, INCEpTION, BERT, DistilBERT, Discourse Parsing, NLP.
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