Inducing Multilingual Text Analysis Tools Using Bidirectional Recurrent Neural Networks
Table 2: Super Sense Tagging (SST) accuracy for Simple Projection, RNN and their combination.
['[BOLD] Model Baseline', '[BOLD] Model', '[BOLD] Italian  [BOLD] MSC-IT-1', '[BOLD] Italian  [BOLD] MSC-IT-2', '[BOLD] French  [BOLD] MSC-FR-1', '[BOLD] French  [BOLD] MSC-FR-2']
SRNN-POS-X and BRNN-POS-X refer to our RNN variants: In means input layer, H1 means first hidden layer and H2 means second hidden layer. We achieve the best performance on Italian using MSC-IT-1 clean corpus while noisy training corpus degrades SST performance. The best results are obtained with combination of simple projection and RNN which confirms (as for POS tagging) that both approaches are complementary.