ABSTRACT The development of computational models for legal reasoning has been a prominent research area for decades. Recently, however, there has been significant progress in enhancing the comprehension of legal contracts through advanced Natural Language Processing (NLP) techniques, particularly transformer‐based models. NLP plays a crucial role in identifying and analysing various types of legal contracts and extracting critical clauses from them. While rule‐based approaches were traditionally dominant, modern deep learning and transformer models are increasingly utilized. These models enable the learning of complex rules that are often difficult for humans to articulate using symbolic or rule‐based systems. Furthermore, ongoing research is exploring neuro‐symbolic models that aim to integrate the strengths of both symbolic and neural approaches. This survey paper identifies gaps in clause relationship linkage and neuro‐symbolic approaches. This survey reviews the techniques and datasets employed in NLP for legal contract analysis, summarizing recent advancements in this field. It emphasizes the evolution of NLP since the introduction of transformer architectures such as GPT‐4, Llama, BERT, XLNet, Gemini and other variants frequently used to address a range of NLP problems. Additionally, it provides an overview of state‐of‐the‐art research that has achieved notable performance in tasks such as clause extraction, document classification, risk assessment, legal question answering and more.
Vuthoo et al. (2026) studied this question.