Papers with few-shot named entity recognition
Reconciliation of Pre-trained Models and Prototypical Neural Networks in Few-shot Named Entity Recognition (2022.findings-emnlp)
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| Challenge: | Existing methods do not consider that pre-trained models contain a prominently large amount of information regarding word frequencies, thus biasing prototypical neural networks against learning word entities. |
| Approach: | They propose a one-line-code normalization method to reconcile such a mismatch with empirical and theoretical grounds and propose 'references' for the model enhancement. |
| Outcome: | The proposed method outperforms the state-of-the-art models on nine benchmark datasets and is comparable to the state of the art. |
Type-Aware Decomposed Framework for Few-Shot Named Entity Recognition (2023.findings-emnlp)
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| Challenge: | Named entity recognition (NER) methods can detect entity spans and classify them into pre-defined categories. |
| Approach: | They propose a type-aware decomposed framework to filter out false spans . they propose 'type-against-type' learning strategy to construct more accurate prototypes based on type names as references. |
| Outcome: | The proposed framework yields state-of-the-art on several benchmarks. |
BANER: Boundary-Aware LLMs for Few-Shot Named Entity Recognition (2025.coling-main)
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| Challenge: | Named Entity Recognition (NER) is a fundamental task in Natural Language Processing (NLP) that aims to detect the entity spans of text and classify them into pre-defined set of entity types. |
| Approach: | They propose a boundary-aware contrastive learning strategy to enhance the LLM’s ability to perceive entity boundaries for generalized entity spans. |
| Outcome: | The proposed framework outperforms prior methods and validates its effectiveness across a range of LLM architectures. |