言语行为是人类交际的基本单位之一，真实语料对揭示言语行为的机制至关重要。然而，因语用功能与形式并非一一对应，依赖形式检索的经典语料库方法存在明显局限。自然语言处理技术的数智化演进为解决言语行为的形义对应难题提供了全新可能。本文综述语料库语言学与自然语言处理技术在言语行为研究中的主要进展，指出二者在言语行为识别任务上具有协同潜力。同时，大语言模型技术有望成为未来语用学研究的常规方法之一。

Speech acts constitute one of the fundamental units of human communication, and investigations based on authentic language data are indispensable for uncovering their underlying mechanisms. However, canonical corpus-based approaches based on formal search suffer from notable limitations due to the non-correspondence between pragmatic functions and linguistic forms. With the advancement of natural language processing, digital intelligence techniques have opened up new avenues for addressing the long-standing problem of the form-function mismatch in speech acts. This paper reviews the major developments in speech act research within both corpus linguistics and natural language processing, and highlights the synergetic potential between the two. Moreover, large language model technologies are expected to serve as one of the mainstream empirical methodologies in future pragmatics research.