Learn and Review: Enhancing Continual Named Entity Recognition via Reviewing Synthetic Samples (2022.findings-acl)
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| Challenge: | Existing methods for named entity recognition classify mentions into fixed set of predefined entity types but in many real-world scenarios, new entity types are incrementally involved. |
| Approach: | They propose a two-stage framework Learn-and-Review for continual named entity recognition to alleviate inter-type confusion. |
| Outcome: | The proposed framework outperforms the state-of-the-art method on CoNLL-03 and OntoNotes-5.0. |
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| Challenge: | Continual learning for named entity recognition (CL-NER) aims to enable models to continuously learn new entity types while retaining the ability to recognize previously learned ones. |
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| Challenge: | Existing methods for Named Entity Recognition (NER) are not able to learn Other-Class in the same way as new entity types. |
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| Challenge: | Named Entity Recognition (NER) is a fundamental and widely used task in natural language processing. |
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Ruotian Ma, Xuanting Chen, Zhang Lin, Xin Zhou, Junzhe Wang, Tao Gui, Qi Zhang, Xiang Gao, Yun Wen Chen
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| Challenge: | Existing named entity recognition systems require large scale labeled data to perform, while annotation of NER data is laborious and time-consuming. |
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| Challenge: | Existing models for Named Entity Recognition (NER) are trained on data with the same NE label set, but they are not able to recognize previously unseen NE categories. |
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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: | Named Entity Recognition (NER) is a fundamental task in Information Extraction. |
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Coarse-to-Fine Pre-training for Named Entity Recognition (2020.emnlp-main)
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| Challenge: | Named Entity Recognition (NER) is a task of discovering information entities and identifying their corresponding categories. |
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