ArticleScientific data2024
AgCNER, the First Large-Scale Chinese Named Entity Recognition Dataset for Agricultural Diseases and Pests.
Article in Scientific data, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- Article
- Bio-Inspired Multi-Granularity Model for Rice Pests and Diseases Named Entity Recognition in Chinese.Biomimetics (Basel, Switzerland) · 2025Article
- Business Intent and Network Slicing Correlation Dataset from Data-Driven Perspective.Scientific data · 2025Article
- Integration of Diffusion Transformer and Knowledge Graph for Efficient Cucumber Disease Detection in Agriculture.Plants (Basel, Switzerland) · 2024Article
- AgCNER, the First Large-Scale Chinese Named Entity Recognition Dataset for Agricultural Diseases and Pests.Scientific data · 2024Article
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5 authors.
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Abstract
Named entity recognition is a fundamental subtask for knowledge graph construction and question-answering in the agricultural diseases and pests field. Although several works have been done, the scarcity of the Chinese annotated dataset has restricted the development of agricultural diseases and pests named entity recognition(ADP-NER). To address the issues, a large-scale corpus for the Chinese ADP-NER task named AgCNER was first annotated. It mainly contains 13 categories, 206,992 entities, and 66,553 samples with 3,909,293 characters. Compared with other datasets, AgCNER maintains the best performance in terms of the number of categories, entities, samples, and characters. Moreover, this is the first publicly available corpus for the agricultural field. In addition, the agricultural language model AgBERT is also fine-tuned and released. Finally, the comprehensive experimental results showed that BiLSTM-CRF achieved F
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