This paper presents a Constraint Grammar-inspired machine learner and parser, LingPars, that assigns dependencies to morphologically annotated treebanks in a function-centred way. The system not only bases attachment probabilities for PoS, case, mood, lemma on those features' function probabilities, but also uses topological features like function/PoS n-grams, barrier tags and daughter-sequences. In the CoNLL shared task, performance was below average on attachment scores, but a relatively higher score for function tags/deprels in isolation suggests that the system's strengths were not fully exploited in the current architecture.