Few-Shot Named Entity Recognition via Meta-Learning

Jing LI, Billy CHIU, Shanshan FENG, Hao WANG

Research output: Journal PublicationsJournal Article (refereed)peer-review

45 Citations (Scopus)


Few-shot learning under the N-way K-shot setting (i.e., K annotated samples for each of N classes) has been widely studied in relation extraction (e.g., FewRel) and image classification (e.g., Mini-ImageNet). Named entity recognition (NER) is typically framed as a sequence labeling problem where the entity classes are inherently entangled together because the entity number and classes in a sentence are not known in advance, leaving the N-way K-shot NER problem so far unexplored. In this paper, we first formally define a more suitable N-way K-shot setting for NER. Then we propose FewNER, a novel meta-learning approach for few-shot NER. FewNER separates the entire network into a task-independent part and a task-specific part. During training in FewNER, the task-independent part is meta-learned across multiple tasks and a task-specific part is learned for each single task in a low-dimensional space. At test time, FewNER keeps the task-independent part fixed and adapts to a new task via gradient descent by updating only the task-specific part, resulting in it being less prone to overfitting and more computationally efficient. The results demonstrate that FewNER achieves state-of-the-art performance against nine baseline methods by significant margins on three adaptation experiments.
Original languageEnglish
Number of pages12
JournalIEEE Transactions on Knowledge and Data Engineering
Early online date17 Nov 2020
Publication statusPublished - Sept 2022
Externally publishedYes


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