In fact, they have different weaknesses: Language identity (and consequently typology) fails to abstract away from language scripts.
in practice, learning algorithms, both linear (e.g., structured perceptron [Collins 2002], MiRA [Crammer and Singer 2003] and structured support vector machine [Taskar, Guestrin, and Koller 2004]) and non-linear (deep neural models) iterate between an inference step and a step of parameter update with respect to a gold standard.