Enhancing Distantly Supervised Named Entity Recognition with Strong Label Guided Lottery Training (2024.lrec-main)
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| Challenge: | Named entity recognition (NER) requires a limited quantity of strongly labeled data . weakly labeles can be acquired through distant supervision, but can cause noise . |
| Approach: | They propose a noise-robust learning framework where safe parameters can be identified . they conduct extensive experiments on multiple datasets and show it outperforms the state-of-the-art methods. |
| Outcome: | The proposed framework outperforms the state-of-the-art methods on weakly labeled data. |
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| Challenge: | Existing work focuses on learning deep NER models with weak supervision without any human annotation. |
| Approach: | They propose a framework that can suppress the noise of the weak labels and fine-tune over the strongly labeled data. |
| Outcome: | The proposed framework outperforms existing methods on Named Entity Recognition tasks with weak supervision and weakly labeled data. |
Noise-Robust Training with Dynamic Loss and Contrastive Learning for Distantly-Supervised Named Entity Recognition (2023.findings-acl)
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| 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. |
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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. |
| 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. |
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Named Entity Recognition without Labelled Data: A Weak Supervision Approach (2020.acl-main)
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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. |
| Approach: | They propose a method to learn NER models in the absence of labelled data through weak supervision by using a broad spectrum of labelling functions to automatically annotate texts from the target domain. |
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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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Named Entity Recognition through Deep Representation Learning and Weak Supervision (2021.findings-acl)
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| Challenge: | Weakly supervised named entity recognition (NER) uses noisy labels to estimate the true labels of a dataset. |
| Approach: | They propose a model to learn optimal assignments of latent NER tags using observed tokens and weak labels provided by labeling functions. |
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Feature-Dependent Confusion Matrices for Low-Resource NER Labeling with Noisy Labels (D19-1)
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| 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. |
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Named Entity Recognition via Noise Aware Training Mechanism with Data Filter (2021.findings-acl)
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| Challenge: | Existing methods for named entity recognition (NER) do not distinguish noisy from hard samples. |
| Approach: | They propose a noise-aware-with-filter method to help model identify noisy samples . they propose 'incomplete trust' loss function which boosts L CRF with a robust term . |
| Outcome: | The proposed method outperforms the existing methods on six real-world Chinese and English NER datasets. |
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. |
| Outcome: | The proposed method can improve on large-scale datasets with a small guidance set. |
Improving the Robustness of Distantly-Supervised Named Entity Recognition via Uncertainty-Aware Teacher Learning and Student-Student Collaborative Learning (2024.findings-acl)
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| Challenge: | Named Entity Recognition (NER) methods require a substantial quantity of high-quality annotation for training models. |
| Approach: | They propose a method to reduce the number of incorrect pseudo labels in self-training . they propose 'uncertainty-aware teacher learning' and 'student-student collaboration' |
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