8 papers with code • 1 benchmarks • 0 datasets

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Most implemented papers

Prompt-Based Metric Learning for Few-Shot NER

achen-qaq/proml 8 Nov 2022

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 14 Feb 2023

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 5 May 2023

Supervised named entity recognition (NER) in the biomedical domain depends on large sets of annotated texts with the given named entities.

Learning In-context Learning for Named Entity Recognition

chen700564/metaner-icl 18 May 2023

M}$, and a new entity extractor can be implicitly constructed by applying new instruction and demonstrations to PLMs, i. e., $\mathcal{ (\lambda .

PromptNER: A Prompting Method for Few-shot Named Entity Recognition via k Nearest Neighbor Search

zhang-mozhi/promptner 20 May 2023

We use prompts that contains entity category information to construct label prototypes, which enables our model to fine-tune with only the support set.

How far is Language Model from 100% Few-shot Named Entity Recognition in Medical Domain

toneli/rt-retrieving-and-thinking 1 Jul 2023

Recent advancements in language models (LMs) have led to the emergence of powerful models such as Small LMs (e. g., T5) and Large LMs (e. g., GPT-4).

A Multi-Task Semantic Decomposition Framework with Task-specific Pre-training for Few-Shot NER

dongguanting/msdp-fewshot-ner 28 Aug 2023

The objective of few-shot named entity recognition is to identify named entities with limited labeled instances.