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Greedy, Joint Syntactic-Semantic Parsing with Stack LSTMs (1606.08954v2)
Published 29 Jun 2016 in cs.CL and cs.AI
Abstract: 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.