Logits-Constrained Framework with RoBERTa for Ancient Chinese NER
This paper presents a Logits-Constrained (LC) framework for Ancient Chinese Named Entity Recognition (NER), evaluated on the EvaHan 2025 benchmark. Our two-stage model integrates GujiRoBERTa for contextual encoding and a differentiable decoding mechanism to enforce valid BMES label transitions. Experiments demonstrate that LC improves performance over traditional CRF and BiLSTM-based approaches, especially in high-label or large-data settings. We also propose a model selection criterion balancing label complexity and dataset size, providing practical guidance for real-world Ancient Chinese NLP tasks.
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Chinese Named Entity RecognitionModel Selectionnamed-entity-recognitionNamed Entity RecognitionNamed Entity Recognition (NER)NERvalidMethods 이 논문이 사용한 방법론
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