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Semantic dependency parsing aims at extracting arcs and semantic role labels for all words in a sentence. In this paper, we propose a semantic dependency parser which is based on Long Short-term Memory and makes heavy use of embeddings of words and POS tags. We describe in detail the implementation of the neural parser, including preprocessing, postprocessing and various input features, and show that the neural parser performs close to the top system in the shared task and is very good at capturing non-local dependencies. We also discuss some issues related to the parser and how to improve it.

OCLSP at SemEval-2016 Task 9: Multilayered LSTM as a Neural Semantic Dependency Parser