  Grammatical relationships (GRs) form an important level of natural language
processing, but different sets of GRs are useful for different purposes.
Therefore, one may often only have time to obtain a small training corpus with
the desired GR annotations. On such a small training corpus, we compare two
systems. They use different learning techniques, but we find that this
difference by itself only has a minor effect. A larger factor is that in
English, a different GR length measure appears better suited for finding simple
argument GRs than for finding modifier GRs. We also find that partitioning the
data may help memory-based learning.
