Multi-Grained Named Entity Recognition
Table 4: F1 scores on CoNLL-2003 devlopement set (DEV) and test set (TEST) for the English NER task. Mean and standard deviation across five runs are reported. Pos tags information are not used.
['[BOLD] MODEL', '[BOLD] CoNLL-2003  [BOLD] DEV', '[BOLD] CoNLL-2003  [BOLD] TEST']
Mean and standard deviation across five runs are reported. that the proposed MGNER model outperforms all the baselines. The models designed for non-overlapping entity detection usually performs better than Nested NER models for the NER task. Our proposed framework outperforms state-of-the-art results both on the NER and Nested NER task. Xu et al. From the ablation study, we can observe that by purely adding the context information, the F1 score on the CoNLL-2003 test set improves from 92.23 to 92.26, and by adding the attention mechanism, the F1 score improves to 92.28.