Enhancing Low-resource Fine-grained Named Entity Recognition by Leveraging Coarse-grained Datasets (2023.emnlp-main)
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| Challenge: | Named Entity Recognition (NER) often suffers from insufficient labeled data when the number of annotations exceeds several tens of labels. |
| Approach: | They propose a model with a fine-to- coarse mapping matrix to leverage hierarchical structure explicitly. |
| Outcome: | The proposed model outperforms both K-shot learning and supervised learning methods when dealing with a small number of fine-grained annotations. |
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Ruotian Ma, Zhang Lin, Xuanting Chen, Xin Zhou, Junzhe Wang, Tao Gui, Qi Zhang, Xiang Gao, Yun Wen Chen
| Challenge: | Existing few-shot NER solutions do not consider sub-class discrimination and various granularity of new classes during coarse training. |
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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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| Challenge: | Named entity recognition models rely on large amounts of labeled data, making them challenging to extend to new, lower-resource languages. |
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| Challenge: | Existing methods for fine-grained entity typing require a large tag set and knowledge of the context. |
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| Challenge: | Existing text classification methods focus on a fixed label set, but many real-world applications require extending to new fine-grained classes as the number of samples per label increases. |
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Few-NERD: A Few-shot Named Entity Recognition Dataset (2021.acl-long)
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| Challenge: | Existing approaches to few-shot named entity recognition (NER) focus on coarse-grained entities with few examples, while most unseen entities are fine-grounded. |
| Approach: | They present a human-annotated few-shot named entity recognition dataset . they construct benchmark tasks to assess the generalization capability of models . |
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An Empirical Study on Fine-Grained Named Entity Recognition (C18-1)
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Khai Mai, Thai-Hoang Pham, Minh Trung Nguyen, Tuan Duc Nguyen, Danushka Bollegala, Ryohei Sasano, Satoshi Sekine
| Challenge: | Named entity recognition (NER) is a well studied topic in natural language processing. |
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| Challenge: | Few-shot named entity recognition (NER) uses only a few annotated examples to identify named entities within text. |
| Approach: | They propose to leverage natural language descriptions of each entity type to perform few-shot named entity recognition. |
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Large Margin Representation Learning for Robust Cross-lingual Named Entity Recognition (2025.acl-long)
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| Challenge: | Existing approaches to name entity recognition neglect distribution skewness and pseudo-label bias . despite promising results, current approaches neglect these problems . |
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NERetrieve: Dataset for Next Generation Named Entity Recognition and Retrieval (2023.findings-emnlp)
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| Challenge: | Named Entity Recognition (NER) is a widely adopted NLP task . authors present three variants of NER task, with dataset to support them . |
| Approach: | They propose three variants of the NER task, together with a dataset to support them . they propose a move towards more fine-grained entities and zero-shot recognition . |
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