基于古汉语预训练模型与外部上下文检索，提升古文命名实体识别准确率。
Named Entity Recognition in Context
- 融合古文预训练模型与外部上下文检索，增强实体理解。
- 在竞赛中实现85.58的平均F1分数，比基线高近5分。
- 适合处理古籍文本中的命名实体识别任务。
我们介绍编辑敦煌团队为EvaHan2025竞赛开发的命名实体识别系统。该方法整合三个核心组件：(1) Pindola，一个基于现代Transformer的双向编码器，使用大量古典汉语文本预训练；(2) 检索模块，为每个目标序列获取相关外部上下文；(3) 生成式推理步骤，将检索到的上下文以古典汉语进行摘要，实现更鲁棒的实体消歧。采用此方法，我们在竞赛中取得平均F1分数85.58，较基线提升近5个百分点。
原文摘要 · Abstract (English)
We present the Named Entity Recognition system developed by the Edit Dunhuang team for the EvaHan2025 competition. Our approach integrates three core components: (1) Pindola, a modern transformer-based bidirectional encoder pretrained on a large corpus of Classical Chinese texts; (2) a retrieval module that fetches relevant external context for each target sequence; and (3) a generative reasoning step that summarizes retrieved context in Classical Chinese for more robust entity disambiguation. Using this approach, we achieve an average F1 score of 85.58, improving upon the competition baseline by nearly 5 points.
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