Certain forms of linguistic annotation, like part of speech and semantic tagging, can be automated with high accuracy. However, manual annotation is still necessary for complex pragmatic and discursive features that lack a direct mapping to lexical forms. This manual process is time-consuming and error-prone, limiting the scalability of function-to-form approaches in corpus linguistics. To address this, our study explores the possibility of using large language models (LLMs) to automate pragma-discursive corpus annotation. We compare GPT-3.5 (the model behind the free-to-use version of ChatGPT), GPT-4 (the model underpinning the precise mode of Bing chatbot), and a human coder in annotating apology components in English based on the local grammar framework. We find that GPT-4 outperformed GPT-3.5, with accuracy approaching that of a human coder. These results suggest that LLMs can be successfully deployed to aid pragma-discursive corpus annotation, making the process more efficient, scalable and accessible.

翻译：某些形式的语言标注，如词性标注和语义标注，已能实现高精度自动化。然而，对于缺乏词汇形式直接映射的复杂语用和话语特征，仍需人工标注。这种人工过程耗时且易出错，限制了功能-形式方法在语料库语言学中的可扩展性。为解决此问题，本研究探索利用大语言模型（LLMs）实现语用-话语语料库标注自动化的可能性。我们基于局部语法框架，比较了GPT-3.5（免费版ChatGPT的底层模型）、GPT-4（必应聊天机器人精确模式的支撑模型）与人工编码者在英语道歉成分标注上的表现。研究发现GPT-4优于GPT-3.5，其准确率接近人类编码者水平。这些结果表明，LLMs可成功应用于辅助语用-话语语料库标注，使该过程更高效、可扩展且易于实施。