Relabel the Noise: Joint Extraction of Entities and Relations via Cooperative Multiagents
Table 2: NER performance on two datasets, 3-time average results with standard deviations are reported.
['[BOLD] Methods', '[BOLD] Wiki-KBP  [BOLD] S-F1', '[BOLD] Wiki-KBP  [BOLD] Ma-F1', '[BOLD] Wiki-KBP  [BOLD] Mi-F1', '[BOLD] BioInfer  [BOLD] S-F1', '[BOLD] BioInfer  [BOLD] Ma-F1', '[BOLD] BioInfer  [BOLD] Mi-F1']
We adopt the Macro-F1, Micro-F1 and Strict-F1 metrics Ling and Weld For Strict-F1, the entity prediction is considered to be “strictly” correct if and only if when the true set of entity tags is equal to the prediction set. Note that the refinements on BioInfer is significant (t-test with p<0.05) even though the BioInfer has a large entity type set (2,200 types) and the base extractor CoType has achieved a high performance (0.74 S-F1), which shows that our agents are capable of leading entity extractors towards a better optimization with noisy.