Few-shot NER

12 papers with code • 3 benchmarks • 3 datasets

This task has no description! Would you like to contribute one?

Most implemented papers

Few-NERD: A Few-Shot Named Entity Recognition Dataset

thunlp/Few-NERD ACL 2021

In this paper, we present Few-NERD, a large-scale human-annotated few-shot NER dataset with a hierarchy of 8 coarse-grained and 66 fine-grained entity types.

Simple and Effective Few-Shot Named Entity Recognition with Structured Nearest Neighbor Learning

asappresearch/structshot EMNLP 2020

We present a simple few-shot named entity recognition (NER) system based on nearest neighbor learning and structured inference.

Template-Based Named Entity Recognition Using BART

Nealcly/templateNER Findings (ACL) 2021

To address the issue, we propose a template-based method for NER, treating NER as a language model ranking problem in a sequence-to-sequence framework, where original sentences and statement templates filled by candidate named entity span are regarded as the source sequence and the target sequence, respectively.

Learning from Miscellaneous Other-Class Words for Few-shot Named Entity Recognition

shuaiwa16/OtherClassNER ACL 2021

Few-shot Named Entity Recognition (NER) exploits only a handful of annotations to identify and classify named entity mentions.

Probing Pre-trained Auto-regressive Language Models for Named Entity Typing and Recognition

deezer/net-ner-probing 26 Aug 2021

The results show: auto-regressive language models as meta-learners can perform NET and NER fairly well especially for regular or seen names; name irregularity when often present for a certain entity type can become an effective exploitable cue; names with words foreign to the model have the most negative impact on results; the model seems to rely more on name than context cues in few-shot NER.

CONTaiNER: Few-Shot Named Entity Recognition via Contrastive Learning

psunlpgroup/container ACL 2022

Named Entity Recognition (NER) in Few-Shot setting is imperative for entity tagging in low resource domains.

An Enhanced Span-based Decomposition Method for Few-Shot Sequence Labeling

wangpeiyi9979/esd 27 Sep 2021

Few-Shot Sequence Labeling (FSSL) is a canonical paradigm for the tagging models, e. g., named entity recognition and slot filling, to generalize on an emerging, resource-scarce domain.

Template-free Prompt Tuning for Few-shot NER

rtmaww/EntLM 28 Sep 2021

Prompt-based methods have been successfully applied in sentence-level few-shot learning tasks, mostly owing to the sophisticated design of templates and label words.

Simple Questions Generate Named Entity Recognition Datasets

dmis-lab/gener 16 Dec 2021

Surprisingly, on NCBI-disease, our model achieves 75. 5 F1 score and even outperforms the previous best weakly supervised model by 4. 1 F1 score, which utilizes a rich in-domain dictionary provided by domain experts.

Few-shot Named Entity Recognition with Self-describing Networks

chen700564/sdnet ACL 2022

In this paper, we propose a self-describing mechanism for few-shot NER, which can effectively leverage illustrative instances and precisely transfer knowledge from external resources by describing both entity types and mentions using a universal concept set.