We explore the performance of two dependency parsing approaches, the rule-based WCDG approach and the data-driven dependency parser MaltParser on texts written by language learners. We show that WCDG outperforms MaltParser in identifying the main functor-argument relations, whereas MaltParser is more successful than WCDG in establishing optional, adjunct dependency relations. This can be interpreted as a tradeoff between the rich, hand-crafted lexical resources capturing obligatory argument relations in WCDG and the ability of a data-driven parser to identify optional, adjunct relations based on the linguistic and world knowledge encoded in the gold-standard training corpora.