In this paper, we set out to present an original rule learning algorithm for symbolic Natural Language Processing (NLP), designed to learn the rules of extraction of keywords marked in its training sentences. What really sets our methodology apart from other recent developments in the field of NLP is the implementation of a hierarchy of parts-of-speech at the very core of the algorithm. This makes the rules dependent only on the sentence's structure rather than on context and domain-specific information. The theoretical development and the experimental results support the conclusion that this improved methodology can be used to obtain an in-depth analysis of the text without being limited to a single domain of application. Consequently, it has the advantage of outperforming both traditional statistical and symbolic NLP methodologies.