  In principle, the design of transition-based dependency parsers makes it
possible to experiment with any general-purpose classifier without other
changes to the parsing algorithm. In practice, however, it often takes
substantial software engineering to bridge between the different
representations used by two software packages. Here we present extensions to
MaltParser that allow the drop-in use of any classifier conforming to the
interface of the Weka machine learning package, a wrapper for the TiMBL
memory-based learner to this interface, and experiments on multilingual
dependency parsing with a variety of classifiers. While earlier work had
suggested that memory-based learners might be a good choice for low-resource
parsing scenarios, we cannot support that hypothesis in this work. We observed
that support-vector machines give better parsing performance than the
memory-based learner, regardless of the size of the training set.
