  Medical professionals search the published literature by specifying the type
of patients, the medical intervention(s) and the outcome measure(s) of
interest. In this paper we demonstrate how features encoding syntactic patterns
improve the performance of state-of-the-art sequence tagging models (both
linear and neural) for information extraction of these medically relevant
categories. We present an analysis of the type of patterns exploited, and the
semantic space induced for these, i.e., the distributed representations learned
for identified multi-token patterns. We show that these learned representations
differ substantially from those of the constituent unigrams, suggesting that
the patterns capture contextual information that is otherwise lost.
