We present a transition-based parser that jointly produces syntactic and semantic dependencies. It learns a representation of the entire algorithm state, using stack long short-term memories. Our greedy inference algorithm has linear time, including feature extraction. On the CoNLL 2008--9 English shared tasks, we obtain the best published parsing performance among models that jointly learn syntax and semantics.
@cite_8: In this paper we describe the CoNLL-2005 shared task on Semantic Role Labeling. We introduce the specification and goals of the task, describe the data sets and evaluation methods, and present a general overview of the 19 systems that have contributed to the task, providing a comparative description and results.
There has also been an increased interest in models which use neural networks for SRL. proposed models which perform many NLP tasks without hand-crafted features. Though they did not achieve the best results on the constituent-based SRL task @cite_8 , their approach inspired , who achieved state-of-the-art results using deep bidirectional LSTMs. Our approach for dependency-based SRL is not directly comparable.