This chapter investigates NLP techniques for ontology population, using a combination of rule-based approaches and machine learning. We describe a method for term recognition using linguistic and statistical techniques, making use of contextual information to bootstrap learning. We then investigate how term recognition techniques can be useful for the wider task of information extraction, making use of similarity metrics and contextual information. We describe two tools we have developed which make use of contextual information to help the development of rules for named entity recognition. Finally, we evaluate our ontology-based information extraction results using a novel technique we have developed which makes use of similarity-based metrics first developed for term recognition.