An Empirical Study of Discriminative Sequence Labeling Models for Vietnamese Text Processing
TABLE III: Performance of LSTMs for PoS tagging using word identities and word shapes
['[BOLD] Hidden Units', '[BOLD] Test Acc.  [ITALIC] n≤20', '[BOLD] Test Acc.  [ITALIC] n≤25', '[BOLD] Training Acc.  [ITALIC] n≤20', '[BOLD] Training Acc.  [ITALIC] n≤25']
We train different LSTMs with varying number hidden units in the range from 32 to 200. We see that the larger number of hidden units is, the better result the tagger can achieve on the test set. The LSTMs tagger achieves 85.98% of accuracy on the test set when the network has 200 hidden units.