CleanCoNLL: A Nearly Noise-Free Named Entity Recognition Dataset (2023.emnlp-main)
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| Challenge: | Existing models achieve F1-scores comparable to or exceed noise level in CoNLL-03 . current models have significant annotation errors, incompleteness, and inconsistencies in the data . |
| Approach: | They propose to add a layer of entity linking annotation to the CoNLL-03 corpus to correct 7.0% of all labels. |
| Outcome: | The proposed approach corrects 7.0% of all labels in the English CoNLL-03 dataset. |
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| Challenge: | a glass ceiling for named entity recognition systems has been suggested for 2021 . however, the performance of the most popular NER benchmarks has plateaued since then . we investigate what NER models are still struggling with . |
| Approach: | They perform a fine-grained evaluation of the model outputs by adding document annotations to the CoNLL-03 English dataset to identify lingering errors. |
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NoiseBench: Benchmarking the Impact of Real Label Noise on Named Entity Recognition (2024.emnlp-main)
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| Challenge: | Existing approaches to named entity recognition often contain a significant percentage of incorrect labels for entity types and boundary boundaries. |
| Approach: | They propose a noise-robust learning approach that learns from data with partially incorrect labels. |
| Outcome: | The proposed methods are based on simulated noise and are easier to handle than simulated real noise caused by human error or semi-automatic annotation. |
Do CoNLL-2003 Named Entity Taggers Still Work Well in 2023? (2023.acl-long)
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| Challenge: | NER models trained on 20-year-old test set may not perform well on modern data. |
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MultiCoNER v2: a Large Multilingual dataset for Fine-grained and Noisy Named Entity Recognition (2023.findings-emnlp)
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| Challenge: | Named Entity Recognition (NER) is a core task in Natural Language Processing. |
| Approach: | They present a dataset for fine-grained Named Entity Recognition covering 33 entity classes across 12 languages in monolingual and multilingual settings. |
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Self-Cleaning: Improving a Named Entity Recognizer Trained on Noisy Data with a Few Clean Instances (2024.findings-naacl)
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| Challenge: | Existing methods to train named entity recognition models on noisy data are expensive and time-intensive to accumulate. |
| Approach: | They propose to denoise noisy NER data with guidance from a small set of clean instances. |
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Named Entity Recognition for Entity Linking: What Works and What’s Next (2021.findings-emnlp)
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| Challenge: | Entity Linking (EL) systems have achieved impressive results on standard benchmarks thanks to the contextualized representations provided by recent pretrained language models. |
| Approach: | They propose to exploit Named Entity Recognition (NER) to narrow the gap between EL systems trained on high and low amounts of labeled data. |
| Outcome: | The proposed model can be exploited to narrow the gap between EL systems trained on high and low amounts of labeled data. |
Noisy-Labeled NER with Confidence Estimation (2021.naacl-main)
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| Challenge: | Recent studies in deep learning have shown significant progress in named entity recognition (NER) . however, most existing works assume clean data annotation, while real-world data typically involve a large amount of noises. |
| Approach: | They propose a confidence estimation approach for named entity recognition using noisy labels using local and global independence assumptions. |
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CrossWeigh: Training Named Entity Tagger from Imperfect Annotations (D19-1)
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| Challenge: | Named entity recognition (NER) models can identify labels in 5.38% of test sentences . a framework to handle label mistakes during NER model training is proposed . |
| Approach: | They propose a framework to manually correct label mistakes in named entity recognition (NER) they aim to improve the accuracy of models by re-evaluating popular models on corrected test sets . |
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NNE: A Dataset for Nested Named Entity Recognition in English Newswire (P19-1)
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| Challenge: | Named entity recognition (NER) is widely used in downstream tasks but most tools focus on flat mention structure over coarse schemas. |
| Approach: | They describe a fine-grained, nested named entity dataset over the Wall Street Journal portion of the Penn Treebank. |
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Constrained Decoding for Computationally Efficient Named Entity Recognition Taggers (2020.findings-emnlp)
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| Challenge: | Named entity recognition models use a conditional random field as the final layer . current work eschews prior knowledge of how the span encoding scheme works . |
| Approach: | They propose to constrain the output to suppress illegal transitions to train a tagger with a cross-entropy loss twice as fast as a CRF. |
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