A Unified MRC Framework for Named Entity Recognition
Table 2: Results for nested NER tasks.
['[BOLD] English ACE 2004  [BOLD] Model', '[BOLD] English ACE 2004  [BOLD] Precision', '[BOLD] English ACE 2004  [BOLD] Rrecall', '[BOLD] English ACE 2004  [BOLD] F1']
We observe huge performance boosts on the nested NER datasets over previous state-of-the-art models, achieving F1 scores of 85.98%, 86.88%, 83.75% and 80.97% on ACE04, ACE05, GENIA and KBP-2017 datasets, which are +1.28%, +2.55%, +5.44% and +6.37% over previous SOTA performances, respectively.