  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 automating pragma-discursive
corpus annotation using large language models (LLMs). We compare ChatGPT, the
Bing chatbot, and a human coder in annotating apology components in English
based on the local grammar framework. We find that the Bing chatbot
outperformed ChatGPT, with accuracy approaching that of a human coder. These
results suggest that AI can be successfully deployed to aid pragma-discursive
corpus annotation, making the process more efficient and scalable. Keywords:
linguistic annotation, function-to-form approaches, large language models,
local grammar analysis, Bing chatbot, ChatGPT
