Arc-Standard Spinal Parsing with Stack-LSTMs

We present a neural transition-based parser for spinal trees, a dependency representation of constituent trees. The parser uses Stack-LSTMs that compose constituent nodes with dependency-based derivations. In experiments, we show that this model adapts to different styles of dependency relations, but this choice has little effect for predicting constituent structure, suggesting that LSTMs induce useful states by themselves.Comment: IWPT 201

Additional file 1: Figure S1: of Preparing a neuropediatric upper limb exergame rehabilitation system for home-use: a feasibility study

Games of the portable YouGrabber system. Eight games are available for the YouGrabber system for home-use. For many games, different control options are available. In the figure we depicted the most common ones. (PNG 901 kb

Additional file 1: Table S1. of Quantifying selective elbow movements during an exergame in children with neurological disorders: a pilot study

Performance and ROC analyses of game scores and SVMC. (DOC 29 kb

Additional file 2: Table S2. of Quantifying selective elbow movements during an exergame in children with neurological disorders: a pilot study

Relationships between expert opinion and various measures for each condition. (DOC 35 kb