Few-shot Named Entity Recognition via Superposition Concept
  Discrimination
Jiawei Chen Hongyu Lin Xianpei Han Yaojie Lu Shanshan Jiang Bin Dong Le Sun
Few-shot NER aims to identify entities of target types with only limited
number of illustrative instances. Unfortunately, few-shot NER is severely
challenged by the intrinsic precise generalization problem, i.e., it is hard to
accurately determine the desired target type due to the ambiguity stemming from
information deficiency. In this paper, we propose Superposition Concept
Discriminator (SuperCD), which resolves the above challenge via an active
learning paradigm. Specifically, a concept extractor is first introduced to
identify superposition concepts from illustrative instances, with each concept
corresponding to a possible generalization boundary. Then a superposition
instance retriever is applied to retrieve corresponding instances of these
superposition concepts from large-scale text corpus. Finally, annotators are
asked to annotate the retrieved instances and these annotated instances
together with original illustrative instances are used to learn FS-NER models.
To this end, we learn a universal concept extractor and superposition instance
retriever using a large-scale openly available knowledge bases. Experiments
show that SuperCD can effectively identify superposition concepts from
illustrative instances, retrieve superposition instances from large-scale
corpus, and significantly improve the few-shot NER performance with minimal
additional efforts.