  This paper introduces a novel model for semantic role labeling that makes use
of neural sequence modeling techniques. Our approach is motivated by the
observation that complex syntactic structures and related phenomena, such as
nested subordinations and nominal predicates, are not handled well by existing
models. Our model treats such instances as sub-sequences of lexicalized
dependency paths and learns suitable embedding representations. We
experimentally demonstrate that such embeddings can improve results over
previous state-of-the-art semantic role labelers, and showcase qualitative
improvements obtained by our method.
