  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.
