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. |
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| Challenge: | Named entity recognition (NER) is a language understanding task that requires large amounts of in-domain labeled data to perform well. |
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| Challenge: | Existing methods to perform few-shot named entity recognition are limited and overfitting is caused by the spurious correlation resulting from the bias in selecting a few samples. |
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| Challenge: | Existing methods for fine-tuning pre-trained language models are limited . we propose a few-shot fine-uning framework for NER . |
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