Few-shot Named Entity Recognition via Superposition Concept Discrimination
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.
Code (1)
Tasks
Active Learningfew-shot-nerFew-shot NERnamed-entity-recognitionNamed Entity RecognitionNERSimilar Papers 제목 키워드 기반
Few-shot Named Entity Recognition with Self-describing Networks
Few-shot NER needs to effectively capture information from limited instances and transfer useful knowledge from external resources. In this paper, we propose a self-describing mechanism for few-shot NER, which can effect…
Few-shot NERNamed Entity RecognitionNamed Entity Recognition (NER)Decomposed Meta-Learning for Few-Shot Named Entity Recognition
Few-shot named entity recognition (NER) systems aim at recognizing novel-class named entities based on only a few labeled examples. In this paper, we present a decomposed meta-learning approach which addresses the proble…
Entity TypingFew-shot NERMeta-LearningNamed Entity Recognition+1A Few-Shot Learning Focused Survey on Recent Named Entity Recognition and Relation Classification Methods
Named Entity Recognition (NER) and Relation Classification (RC) are important steps in extracting information from unstructured text and formatting it into a machine-readable format. We present a survey of recent deep le…
ClassificationFew-Shot Learninggraph constructionInformation Retrieval+9Label Embedding for Zero-shot Fine-grained Named Entity Typing
Named entity typing is the task of detecting the types of a named entity in context. For instance, given {``}Eric is giving a presentation{''}, our goal is to infer that {`}Eric{'} is a speaker or a presenter and a perso…
Entity LinkingEntity TypingNamed Entity Recognition (NER)Question Answering+2BANER: Boundary-Aware LLMs for Few-Shot Named Entity Recognition
Despite the recent success of two-stage prototypical networks in few-shot named entity recognition (NER), challenges such as over/under-detected false spans in the span detection stage and unaligned entity prototypes in …
Contrastive Learningdomain classificationnamed-entity-recognitionNamed Entity Recognition+2