DynamicNER: A Dynamic, Multilingual, and Fine-Grained Dataset for LLM-based Named Entity Recognition (2025.emnlp-main)
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Hanjun Luo, Yingbin Jin, Yiran Wang, Xinfeng Li, Tong Shang, Xuecheng Liu, Ruizhe Chen, Kun Wang, Hanan Salam, Qingsong Wen, Zuozhu Liu
| Challenge: | Existing datasets designed for Named Entity Recognition methods are inadequate for LLMs. |
| Approach: | They propose a dataset that is multilingual and multi-granular and enables LLMs to be applied to Named Entity Recognition methods. |
| Outcome: | The proposed dataset is multilingual and multi-granular, covering 8 languages and 155 entity types, with corpora spanning a diverse range of domains. |
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| Challenge: | Named Entity Recognition (NER) is a process of identifying named entities in unstructured texts and classifying them through specific semantic categories. |
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| Challenge: | Named Entity Recognition (NER) is a core task in Natural Language Processing. |
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| Challenge: | Named entity recognition (NER) is the task of identifying tokens that belong to a predefined set of classes such as "person" or "location" |
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| Challenge: | Named Entity Recognition (NER) is a core task in Natural Language Processing. |
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Yuming Yang, Wantong Zhao, Caishuang Huang, Junjie Ye, Xiao Wang, Huiyuan Zheng, Yang Nan, Yuran Wang, Xueying Xu, Kaixin Huang, Yunke Zhang, Tao Gui, Qi Zhang, Xuanjing Huang
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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 . |
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OpenNER 1.0: Standardized Open-Access Named Entity Recognition Datasets in 50+ Languages (2025.emnlp-main)
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| Challenge: | Existing datasets are not consistently formatted and use a variety of chunk encodings (IOB, BIO, etc.), often without documentation. |
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| Challenge: | Named entity recognition (NER) is a challenging task in natural language processing . nested NER requires sophisticated techniques to identify entities within entities . |
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ProgGen: Generating Named Entity Recognition Datasets Step-by-step with Self-Reflexive Large Language Models (2024.findings-acl)
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| Challenge: | Large Language Models (LLMs) exhibit remarkable adaptability across domains, but they are often not suitable for structured knowledge extraction tasks such as named entity recognition (NER). |
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LLMs as Bridges: Reformulating Grounded Multimodal Named Entity Recognition (2024.findings-acl)
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| Challenge: | Existing methods for Grounded Multimodal Named Entity Recognition (GMNER) lack a strong correlation between image-text pairs and is ungroundable. |
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