Since the manual construction of ontologies is time-consuming and expensive, an increasing number of initiatives to ease the construction by automatic or semi-automatic means have been published. Most initiatives combine a certain level of NLP techniques with machine learning approaches to find concepts and relationships. However, a challenging issue is to quantitatively evaluate the usefulness or accuracy of the techniques and combinations of techniques when applied to ontology learning. We are developing a framework for acquiring an ontology from a large collection of domain texts. This framework provides support for evaluating different NLP and machine learning techniques when they are applied to ontology learning. Our initial experiment supports our assumptions on the usefulness of our approach.