We present a dynamic programming algorithm
for efficient constrained inference in semantic
role labeling. The algorithm tractably captures
a majority of the structural constraints examined
by prior work in this area, which has resorted
to either approximate methods or off-theshelf
integer linear programming solvers. In addition,
it allows training a globally-normalized
log-linear model with respect to constrained
conditional likelihood. We show that the dynamic
program is several times faster than an
off-the-shelf integer linear programming solver,
while reaching the same solution. Furthermore,
we show that our structured model results in
significant improvements over its local counterpart,
achieving state-of-the-art results on both
PropBank- and FrameNet-annotated corpora.

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