A Hybrid Method for Low-Resource Named Entity Recognition
Named Entity Recognition (NER) is a critical component of Natural Language Processing with diverse applications in information extraction and conversational AI. However, NER in specific domains for low-resource languages faces challenges such as limited annotated data and heterogeneous label sets. This study addresses these issues by proposing a hybrid neurosymbolic framework that integrates rule-based processing with deep learning models for Vietnamese NER. The core idea involves a two-stage pipeline: first, a rule-based component reduces label complexity by grouping relational and special categories; second, pre-trained language models are fine-tuned for high-precision extraction. A pos...
| Authors | Do Minh Duc, Quan Xuan Truong, Viet Tran Hong et al. |
|---|---|
| Primary category | cs.CE |
| Published | 6 May 2026 |
| Last updated at source | 6 May 2026 |
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