  We investigate the use of different syntactic dependency representations in a
neural relation classification task and compare the CoNLL, Stanford Basic and
Universal Dependencies schemes. We further compare with a syntax-agnostic
approach and perform an error analysis in order to gain a better understanding
of the results.
