  This article provides an understanding of Natural Language Processing
techniques in the framework of financial regulation, more specifically in order
to perform semantic matching search between rules and policy when no dataset is
available for supervised learning. We outline how to outperform simple
pre-trained sentences-transformer models using freely available resources and
explain the mathematical concepts behind the key building blocks of Natural
Language Processing.
