Multi-Grained Named Entity Recognition
Table 2: Performance on ACE-2004 and ACE-2005 test set for the Nested NER task.
['[BOLD] MODEL', '[BOLD] ACE-2004  [BOLD] P', '[BOLD] ACE-2004  [BOLD] R', '[BOLD] ACE-2004  [BOLD] F1', '[BOLD] ACE-2005  [BOLD] P', '[BOLD] ACE-2005  [BOLD] R', '[BOLD] ACE-2005  [BOLD] F1']
Experiment results of the Nested NER task on the ACE-2004 and For both datasets, our model improves the state-of-the-art result by around 4% in terms of precision, recall, as well as the F1 score. MGNER w/o attention is a variation of MGNER which removes the self-attention mechanism and MGNER w/o context removes all the context information. To remove the self-attention mechanism, we feed the context feature C directly into a bi-directional LSTM to obtain context representation m, other than the attentive context vector Catt. As for MGNER w/o context, we only use entity representation e to do classification other than the context-aware entity representation o. By adding the context information, the F1 score improves 0.9% on the ACE-2004 dataset and 0.7% on the ACE-2005 dataset. The self-attention mechanism improves the F1 score by 0.6% on the ACE-2004 dataset and 0.5% on the ACE-2005 dataset.