NoiseBench: Benchmarking the Impact of Real Label Noise on Named Entity Recognition (2024.emnlp-main)
Copied to clipboard
| 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. |
Similar Papers
CleanCoNLL: A Nearly Noise-Free Named Entity Recognition Dataset (2023.emnlp-main)
Copied to clipboard
| 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. |
Noisy-Labeled NER with Confidence Estimation (2021.naacl-main)
Copied to clipboard
| 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. |
| Outcome: | The proposed method marginalizes out labels of low confidence with a CRF model and integrates it into a self-training framework for boosting performance. |
NoisywikiHow: A Benchmark for Learning with Real-world Noisy Labels in Natural Language Processing (2023.findings-acl)
Copied to clipboard
| Challenge: | Large-scale datasets in the real world often contain label noise, which can cause model overfitting and degrade generalization. |
| Approach: | They propose to use label noise to imitate human errors in annotations . they use a noisy label noise benchmark to evaluate their methods . |
| Outcome: | The proposed benchmarks are different from data with heterogeneous label noises in the real world. |
Feature-Dependent Confusion Matrices for Low-Resource NER Labeling with Noisy Labels (D19-1)
Copied to clipboard
| Challenge: | Existing approaches to improve supervised labeling with noisy training data do not take the input features into account or they need to learn the noise modeling from scratch. |
| Approach: | They propose to cluster training data using input features and compute different confusion matrices for each cluster. |
| Outcome: | The proposed model improves on low-resource named entity recognition settings in several languages, compared with other models which do not take the input features into account or need to learn noise modeling from scratch. |
Noise-Robust Training with Dynamic Loss and Contrastive Learning for Distantly-Supervised Named Entity Recognition (2023.findings-acl)
Copied to clipboard
| Challenge: | Named entity recognition (NER) is a task in natural language processing that aims at locating entity mentions in a given sentence and assigning them to certain types. |
| Approach: | They propose to use a dynamic loss function to better adapt to the changing noise during the training process and incorporate token level contrastive learning to fully utilize the noisy data. |
| Outcome: | The proposed method outperforms existing NER models on three benchmark datasets and outperformed existing models by significant margins. |
Self-Cleaning: Improving a Named Entity Recognizer Trained on Noisy Data with a Few Clean Instances (2024.findings-naacl)
Copied to clipboard
| 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. |
| Outcome: | The proposed method can improve on large-scale datasets with a small guidance set. |
Token-Level Metrics for Detecting Incorrect Gold Annotations in Named Entity Recognition (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Annotated datasets for supervised learning often contain incorrect labels, i.e. label noise. |
| Approach: | They compare popular sample metrics for detecting incorrect annotations in named entity recognition (NER) they find that training dynamics metrics work the best overall, and they detect errors that the model has not yet memorized . |
| Outcome: | The proposed measures reduce label noise across noise types by detecting errors in trained models. |
CrossWeigh: Training Named Entity Tagger from Imperfect Annotations (D19-1)
Copied to clipboard
| 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 . |
| Outcome: | The proposed framework can detect label mistakes in 5.38% of test sentences . the proposed framework improves on three datasets with a high-performance model . |
MultiCoNER v2: a Large Multilingual dataset for Fine-grained and Noisy Named Entity Recognition (2023.findings-emnlp)
Copied to clipboard
| 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. |
| Outcome: | The proposed dataset covers 33 entity classes across 12 languages in monolingual and multilingual settings. |
Distantly-Supervised Named Entity Recognition with Noise-Robust Learning and Language Model Augmented Self-Training (2021.emnlp-main)
Copied to clipboard
| Challenge: | Named entity recognition models require abundant high-quality annotations to train . distant supervision may induce incomplete and noisy labels, making supervised learning ineffective. |
| Approach: | They propose a noise-robust learning scheme for training named entity recognition models using only distantly-labeled data and a self-training method that uses contextualized augmentations created by pre-trained language models. |
| Outcome: | The proposed method outperforms existing supervised NER models on three datasets by significant margins. |