Logits-Constrained Framework with RoBERTa for Ancient Chinese NER
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915275188731904 |
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| author | Hua, Wenjie Xu, Shenghan |
| author_facet | Hua, Wenjie Xu, Shenghan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_02983 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Logits-Constrained Framework with RoBERTa for Ancient Chinese NER Hua, Wenjie Xu, Shenghan Computation and Language 68T50 I.2.7; I.5.1; I.5.4 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. |
| title | Logits-Constrained Framework with RoBERTa for Ancient Chinese NER |
| topic | Computation and Language 68T50 I.2.7; I.5.1; I.5.4 |
| url | https://arxiv.org/abs/2505.02983 |