  We reduce the task of (span-based) PropBank-style semantic role labeling
(SRL) to syntactic dependency parsing. Our approach is motivated by our
empirical analysis that shows three common syntactic patterns account for over
98% of the SRL annotations for both English and Chinese data. Based on this
observation, we present a conversion scheme that packs SRL annotations into
dependency tree representations through joint labels that permit highly
accurate recovery back to the original format. This representation allows us to
train statistical dependency parsers to tackle SRL and achieve competitive
performance with the current state of the art. Our findings show the promise of
syntactic dependency trees in encoding semantic role relations within their
syntactic domain of locality, and point to potential further integration of
syntactic methods into semantic role labeling in the future.
