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.
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.
@cite_8
2126851059
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.