In this paper we compare different approaches to extract definitions of four types using a combination of a rule-based grammar and machine learning. We collected a Dutch text corpus containing 549 definitions and applied a grammar on it. Machine learning was then applied to improve the results obtained with the grammar. Two machine learning experiments were carried out. In the first experiment, a standard classifier and a classifier designed specifically to deal with imbalanced datasets are compared. The algorithm designed specifically to deal with imbalanced datasets for most types outperforms the standard classifier. .
