A Feature-Rich Vietnamese Named-Entity Recognition Model
Table 8: Impact of word representation-based features. w2v denotes features based on word embeddings. “cluster” denotes cluster-based features.
['[BOLD] Setting', '[BOLD] Precision', '[BOLD] Recall', '[ITALIC] F1']
In order to evaluate the impact of word representation-based features, we conducted experiments with different feature sets. We start with a feature set, then remove features related to word clusters and word vectors. Incorporating those features improves F1 score more than 2%. Resuls also showed that Brown cluster-based features contribute more to the system improvement than word-embedding features. An advantage of word-representations is that they can be learned in the unsupervised fashion from raw-text corpora.