Papers with few-shot NER
Robustness of Demonstration-based Learning Under Limited Data Scenario (2022.emnlp-main)
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| Challenge: | Current large pretrained language models struggle to learn NLP tasks under limited data scenarios. |
| Approach: | They propose to augment input with some demonstrations to improve model performance under limited data scenarios. |
| Outcome: | The proposed demonstrations improve performance on few-shot NER tasks and show that the length of demonstrations and relevance of random tokens are the main factors affecting the model's performance. |
Decomposed Meta-Learning for Few-Shot Named Entity Recognition (2022.findings-acl)
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| Challenge: | Named entity recognition systems aim at recognizing unseen entity types based on a few labeled examples. |
| Approach: | They propose a decomposed meta-learning approach to solve few-shot span detection and few- shot entity typing problems by introducing a model-agnostic meta-loop algorithm. |
| Outcome: | The proposed approach achieves superior performance over prior methods on benchmarks. |
A Streamlined Span-based Factorization Method for Few Shot Named Entity Recognition (2024.lrec-main)
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| Challenge: | Existing approaches to few-shot named entity recognition require large amounts of labeled data. |
| Approach: | They propose a streamlined span-based factorization method that solves few-shot NER problem . they propose to decompose the span-level alignment problem into several refined procedures . |
| Outcome: | The proposed method achieves an average F1 score improvement of 12 points on the FewNERD dataset and 10 points on SNIPS dataset. |
Probing Pre-trained Auto-regressive Language Models for Named Entity Typing and Recognition (2022.lrec-1)
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| Challenge: | Existing studies have focused on auto-regressive models for generalization in named entity (NE) typing (NET) and recognition (NER) . however, little has been done in this direction for auto-Regressive LMs despite their popularity and potential to express a wide variety of NLP tasks in the same unified format. |
| Approach: | They propose to probe auto-regressive LMs for NET and NER generalization by resorting to meta-learning to assess the model's memorization of NEs. |
| Outcome: | The proposed model performs well on NET and NER generalization tasks, while relying more on NE than contextual cues in few-shot NER. |
Focusing, Bridging and Prompting for Few-shot Nested Named Entity Recognition (2023.findings-acl)
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| Challenge: | Existing work on few-shot named entity recognition (NER) addresses flat entities instead of nested entities. |
| Approach: | They propose a method based on focusing, bridging and prompting for few-shot nested NER without using source domain data. |
| Outcome: | The proposed method outperforms baseline models on four benchmark datasets and outperformed several competing models on F1-score by 9.33% on ACE2004, 6.17% on ace2005, 9.40% on GENIA and 5.12% on KBP2017. |
Large-Scale Label Interpretation Learning for Few-Shot Named Entity Recognition (2024.eacl-long)
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| Challenge: | Few-shot named entity recognition (NER) uses only a few annotated examples to identify named entities within text. |
| Approach: | They propose to leverage natural language descriptions of each entity type to perform few-shot named entity recognition. |
| Outcome: | The proposed model learns to interpret verbalized descriptions of entities using natural language descriptions of their types and their verbalizations. |
Task-adaptive Label Dependency Transfer for Few-shot Named Entity Recognition (2023.findings-acl)
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| Challenge: | Existing methods for Named Entity Recognition (NER) ignore label dependency, resulting in suboptimal performance. |
| Approach: | They propose a meta-learning method to make label dependency transferable by learning general initialization and individual parameter update rule for label dependency. |
| Outcome: | The proposed method improves existing methods by learning general initialization and individual parameter update rule for label dependency. |
COPNER: Contrastive Learning with Prompt Guiding for Few-shot Named Entity Recognition (2022.coling-1)
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| Challenge: | Existing methods for Named Entity Recognition (NER) use a similarity metric to measure semantic similarity between test samples and referents, but their performance is limited due to the label scarcity. |
| Approach: | They propose a novel approach to learn a similarity metric for measuring the semantic similarity between test samples and referents, where each referent represents an entity class. |
| Outcome: | The proposed approach outperforms state-of-the-art models with a significant margin in most cases. |
SpanProto: A Two-stage Span-based Prototypical Network for Few-shot Named Entity Recognition (2022.emnlp-main)
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| Challenge: | Existing methods for few-shot Named Entity Recognition ignore entity boundaries and are time-consuming . a seminal span-based prototypical network solves the problem using two stages: span extraction and mention classification. |
| Approach: | They propose a seminal span-based prototypical network that tackles few-shot NER . they transform sequential tags into a global boundary matrix and use prototypical learning . |
| Outcome: | The proposed model outperforms strong baselines over multiple benchmarks. |
Few-TK: A Dataset for Few-shot Scientific Typed Keyphrase Recognition (2024.findings-naacl)
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| Challenge: | Named Entities are a common form of Information Extraction (IE) tasks for scientific texts. |
| Approach: | They propose a rechristening of Named Entities as Typed Keyphrases (TK) they advocate for exploring this task in the few-shot domain due to the scarcity of labeled scientific IE data. |
| Outcome: | The proposed dataset includes scientific Typed Keyphrase annotations on abstracts of 500 research papers. |
Few-shot Named Entity Recognition with Self-describing Networks (2022.acl-long)
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| Challenge: | Existing few-shot named entity recognition (NER) models capture information from limited instances while transferring useful knowledge from external resources. |
| Approach: | They propose a self-describing mechanism for few-shot NER which can universally describe mentions using concepts and automatically map novel entity types to concepts. |
| Outcome: | The proposed model can universally describe mentions using concepts and automatically map novel entity types to concepts and adaptively recognize entities on-demand. |
Simple Questions Generate Named Entity Recognition Datasets (2022.emnlp-main)
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| Challenge: | Recent named entity recognition models rely on human-annotated datasets . however, in-domain dictionaries and sentences are often unavailable or expensive to construct for many entity types. |
| Approach: | They propose an ask-to-generate approach which automatically generates NER datasets by asking natural language questions to an open-domain question answering system. |
| Outcome: | The proposed model outperforms the previous best model by 19.5 F1 score on six benchmarks and achieves state-of-the-art performance. |
FactMix: Using a Few Labeled In-domain Examples to Generalize to Cross-domain Named Entity Recognition (2022.coling-1)
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| Challenge: | Existing approaches for few-shot Named Entity Recognition (NER) are evaluated mainly under in-domain settings, but little is known about how these models perform in cross-domain NER using labeled in- domain examples. |
| Approach: | They propose to use a rationale-centric data augmentation method to improve model generalization ability by allowing model to learn from a few labeled examples in a new target domain. |
| Outcome: | The proposed method improves the performance of cross-domain NER tasks compared to the counterfactual data augmentation and prompt-tuning methods. |
Learning from Miscellaneous Other-Class Words for Few-shot Named Entity Recognition (2021.acl-long)
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| Challenge: | Existing methods to classify named entity mentions with fewshots fail to differentiate rich semantics in other-class words, which will aggravate overfitting under few shot scenario. |
| Approach: | They propose a model that can automatically induce different unde- fined classes from the other class to improve few-shot Named Entity Recognition (NER) . |
| Outcome: | The proposed model outperforms five state-of-the-art models in 1- shot and 5-shots settings on four NER bench marks. |
Designing Informative Metrics for Few-Shot Example Selection (2024.findings-acl)
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| Challenge: | Pretrained language models (PLMs) have shown remarkable few-shot learning capabilities when provided with properly formatted examples. |
| Approach: | They propose a complexity-based prompt selection approach for sequence tagging tasks that uses certain metrics to align the syntactico-semantic complexity of test sentences and examples. |
| Outcome: | The proposed approach achieves state-of-the-art performance on few-shot NER, with 5% improvement in F1 score. |
Few-shot domain adaptation for named-entity recognition via joint constrained k-means and subspace selection (2025.coling-main)
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| Challenge: | Named-entity recognition (NER) requires large annotated datasets, which limits its applicability across domains with varying entity definitions. |
| Approach: | They propose a weakly-supervised algorithm that combines small labeled datasets with large amounts of unlabeled data. |
| Outcome: | The proposed approach achieves state-of-the-art results in few-shot NER . it combines label supervision, cluster size constraints, and domain-specific discriminative subspace selection. |
Know-Adapter: Towards Knowledge-Aware Parameter-Efficient Transfer Learning for Few-shot Named Entity Recognition (2024.lrec-main)
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| Challenge: | Named entity recognition (NER) is a fundamental task in natural language processing. |
| Approach: | They propose a knowledgeable adapter to incorporate structure and semantic knowledge of knowledge graphs into PLMs for few-shot NER. |
| Outcome: | The proposed adapter improves the quality of retrieved information by adding explicit knowledge from external sources to PEFTs. |