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. |
| Outcome: | The proposed approach improves on two English datasets and shows that it improves by 7 percentage points on entity-level F1 scores compared to an out-of-domain neural NER model. |
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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: | 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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| Challenge: | Existing NER models are supervised by a large number of training sequences, each pre-annotated with token-level labels. |
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| Challenge: | Existing approaches to named entity recognition (NER) assume that the training data is fully annotated with named entity information. |
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| Challenge: | Existing methods to build reliable named entity recognition systems require large amounts of manually-labeled training data. |
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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. |
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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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