Most approaches to ontology learning combine techniques from different areas (hybrid approaches) to increase the efficiency of the ontology learning process. However, the results from the ontology learning process do not fully satisfy the users at present. In this context, an important problem is that there is a lack of quantitative and comparative data about the efficiency of techniques and technique combinations applied to ontology learning. Combination methods are an effective way of improving system performance, but there is not enough information about how to use, configure and combine techniques from a diverse spectrum of fields, and what the contribution of a specific technique or technique combination is. In this paper we present a quantitative comparison of technique combinations for concept extraction and a software system (OntoLancs) to support the evaluation of techniques. By applying OntoLancs, users are able to assist the process of building ontologies by semi-automatically acquiring concepts from large-scale domain document collections and experiment with different combinations of knowledge acquisition techniques to refine and organize domain concepts into a taxonomy. Quantitative and comparative studies about the performance of several techniques and user experiences indicate the applicability and usefulness of our approach.