This paper deals with the task of definition extraction with the training corpus suffering from the problems of small size, high noise and heavy imbalance. A previous approach, based on manually constructed shallow grammars, turns out to be hard to better even by such robust classifiers as SVMs, AdaBoost and simple ensembles of classifiers. However, a linear combination of various such classifiers and manual grammars significantly improves the results of the latter.