ELLEN: Extremely Lightly Supervised Learning for Efficient Named Entity Recognition (2024.lrec-main)
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| Challenge: | Named entity recognition (NER) tasks require an amount of annotations that are unrealistic for many real-world applications. |
| Approach: | They propose a semi-supervised named entity recognition method that blends language models with linguistic rules. |
| Outcome: | The proposed method outperforms most existing semi-supervised methods under the same supervision settings commonly used in the literature. |
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| Challenge: | Named entity recognition (NER) problem is performed under extremely weak supervision . XWS setting is considered weaker than 1-shot since example entity is given in context-free way . |
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| Challenge: | Named Entity Recognition (NER) performance often degrades when applied to target domains that differ from the texts observed during training. |
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| Challenge: | Weakly supervised named entity recognition (NER) uses noisy labels to estimate the true labels of a dataset. |
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| Challenge: | Existing work focuses on learning deep NER models with weak supervision without any human annotation. |
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| Challenge: | Named entity recognition models rely on large amounts of labeled data, making them challenging to extend to new, lower-resource languages. |
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Distantly-Supervised Named Entity Recognition with Noise-Robust Learning and Language Model Augmented Self-Training (2021.emnlp-main)
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| Challenge: | Named entity recognition models require abundant high-quality annotations to train . distant supervision may induce incomplete and noisy labels, making supervised learning ineffective. |
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Learning from Language Description: Low-shot Named Entity Recognition via Decomposed Framework (2021.findings-emnlp)
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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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Distantly Supervised Named Entity Recognition using Positive-Unlabeled Learning (P19-1)
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| Challenge: | Empirical studies on four public NER datasets demonstrate the effectiveness of our proposed method. |
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