Few-shot NER
12 papers with code • 3 benchmarks • 3 datasets
Benchmarks
These leaderboards are used to track progress in Few-shot NER
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WNUT 2017
Few-NERD
Most implemented papers
Few-NERD: A Few-Shot Named Entity Recognition Dataset
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
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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
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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
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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
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
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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
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
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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
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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
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.
