Several adaptations of Transformers models have been developed in various domains since its breakthrough in Natural Language Processing (NLP). This trend has spread into the field of Music Information Retrieval (MIR), including studies processing music data. However, the practice of leveraging NLP tools for symbolic music data is not novel in MIR. Music has been frequently compared to language, as they share several similarities, including sequential representations of text and music. These analogies are also reflected through similar tasks in MIR and NLP. This survey reviews NLP methods applied to symbolic music generation and information retrieval studies following two axes. We first propose an overview of representations of symbolic music adapted from natural language sequential representations. Such representations are designed by considering the specificities of symbolic music. These representations are then processed by models. Such models, possibly originally developed for text and adapted for symbolic music, are trained on various tasks. We describe these models, in particular deep learning models, through different prisms, highlighting music-specialized mechanisms. We finally present a discussion surrounding the effective use of NLP tools for symbolic music data. This includes technical issues regarding NLP methods and fundamental differences between text and music, which may open several doors for further research into more effectively adapting NLP tools to symbolic MIR.

翻译：自Transformer模型在自然语言处理领域取得突破以来，其多种变体已在各领域得到发展。这一趋势已扩展至音乐信息检索领域，包括针对音乐数据的处理研究。然而，在音乐信息检索中利用自然语言处理工具处理符号音乐数据的实践并非新事。音乐常被与语言类比，因为两者共享若干相似性，包括文本与音乐的序列化表征。这些类比也通过音乐信息检索与自然语言处理中的类似任务得以体现。本综述沿两条主线回顾了应用于符号音乐生成与信息检索研究中的自然语言处理方法。我们首先概述了从自然语言序列表征改编而来的符号音乐表征形式，此类表征通过考虑符号音乐的特异性而设计。随后，这些表征由各类模型处理——这些模型可能最初为文本开发，后经改编用于符号音乐，并在多种任务上进行训练。我们通过不同视角描述这些模型（特别是深度学习模型），重点突出音乐专用机制。最后，我们围绕自然语言处理工具在符号音乐数据中的有效应用展开讨论，涵盖自然语言处理方法的技术问题以及文本与音乐间的根本差异，这或将为进一步研究如何更有效地将自然语言处理工具适配至符号音乐信息检索开辟多条路径。