An Empirical Study of Discriminative Sequence Labeling Models for Vietnamese Text Processing
TABLE V: Performance of the CRF model for PoS tagging with two feature sets
['[BOLD] Feature set', '[BOLD] Test Acc.  [ITALIC] n≤20', '[BOLD] Test Acc.  [ITALIC] n≤25', '[BOLD] Training Acc.  [ITALIC] n≤20', '[BOLD] Training Acc.  [ITALIC] n≤25']
The table indicates that incorporating word embedding features helps to improves the accuracy of the CRF model 1.35% from 87.62% to 88.97%. The CRF model outperformed LSTMs when we do not use word embedding features. However, its accuracy is lower than that of LSTMs when word embedding features are incorporated.