few-shot-ner
10 papers with code • 1 benchmarks • 0 datasets
Benchmarks
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Most implemented papers
Learning In-context Learning for Named Entity Recognition
M}$, and a new entity extractor can be implicitly constructed by applying new instruction and demonstrations to PLMs, i. e., $\mathcal{ (\lambda .
Prompt-Based Metric Learning for Few-Shot NER
achen-qaq/proml
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Few-shot named entity recognition (NER) targets generalizing to unseen labels and/or domains with few labeled examples.
Meta-Learning Triplet Network with Adaptive Margins for Few-Shot Named Entity Recognition
hccngu/metnet
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We design an improved triplet network to map samples and prototype vectors into a low-dimensional space that is easier to be classified and propose an adaptive margin for each entity type.
From Zero to Hero: Harnessing Transformers for Biomedical Named Entity Recognition in Zero- and Few-shot Contexts
br-ai-ns-institute/zero-shotner
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Supervised named entity recognition (NER) in the biomedical domain depends on large sets of annotated texts with the given named entities.
PromptNER: A Prompting Method for Few-shot Named Entity Recognition via k Nearest Neighbor Search
We use prompts that contains entity category information to construct label prototypes, which enables our model to fine-tune with only the support set.
A Multi-Task Semantic Decomposition Framework with Task-specific Pre-training for Few-Shot NER
dongguanting/msdp-fewshot-ner
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The objective of few-shot named entity recognition is to identify named entities with limited labeled instances.
Generalizing Few-Shot Named Entity Recognizers to Unseen Domains with Type-Related Features
wzh-nlp/pltr
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To address this limitation, recent studies enable generalization to an unseen target domain with only a few labeled examples using data augmentation techniques.
Robust Few-Shot Named Entity Recognition with Boundary Discrimination and Correlation Purification
However, the present few-shot NER models assume that the labeled data are all clean without noise or outliers, and there are few works focusing on the robustness of the cross-domain transfer learning ability to textual adversarial attacks in Few-shot NER.
Few-shot Named Entity Recognition via Superposition Concept Discrimination
Then a superposition instance retriever is applied to retrieve corresponding instances of these superposition concepts from large-scale text corpus.
