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Few-shot named entity recognition (NER) enables us to build a NER system fora new domain using very few labeled examples. However, existing prototypicalnetworks for this task suffer from roughly estimated label dependency andclosely distributed prototypes, thus often causing misclassifications. Toaddress the above issues, we propose EP-Net, an Entity-level PrototypicalNetwork enhanced by dispersedly distributed prototypes. EP-Net buildsentity-level prototypes and considers text spans to be candidate entities, soit no longer requires the label dependency. In addition, EP-Net trains theprototypes from scratch to distribute them dispersedly and aligns spans toprototypes in the embedding space using a space projection. Experimentalresults on two evaluation tasks and the Few-NERD settings demonstrate thatEP-Net consistently outperforms the previous strong models in terms of overallperformance. Extensive analyses further validate the effectiveness of EP-Net.

Few-shot Named Entity Recognition with Entity-level Prototypical Network Enhanced by Dispersedly Distributed Prototypes