This article introduces a novel transition system for discontinuous lexicalized con stituent parsing called sr-gap. It is an ex tension of the shift-reduce algorithm with an additional gap transition. Evaluation on two German treebanks shows that sr-gap outperforms the previous best transition-based discontinuous parser (Maier, 2015) by a large margin (it is notably twice as ac curate on the prediction of discontinuous constituents), and is competitive with the state of the art (Fernandez-Gonzalez and Martins, 2015). As a side contribution, we adapt span features (Hall et al., 2014) to discontinuous parsing.
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