Hierarchically-Refined Label Attention Network for Sequence Labeling
Table 8: F1-scores on the OntoNotes 5.0 test set. * denotes semi-supervised and multi-task learning.
['[BOLD] Model', '[BOLD] F1']
OntoNotes 5.0. In NER, BiLSTM-CRF is widely used, because local dependencies between neighboring labels relatively more important that POS tagging and CCG supertagging. BiLSTM-LAN also significantly outperforms BiLSTM-CRF by 1.17 F1-score (p<0.01). Shen et al. Strubell et al. Clark et al. BiLSTM-LAN obtains highly competitive results compared with various of top-performance models without training on external data.