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数字人文视域下多粒度特征融合的古文命名实体识别
Multi-Granularity Feature Fusion for Named Entity Recognition of Classical Chinese Texts from the Perspective of Digital Humanities
[目的/意义] 利用命名实体识别技术深入挖掘古籍文献，推动中文古籍数字化进程，对于推动历史学习、增强文化自信以及弘扬中国传统文化具有重要意义。[方法/过程] 提出多粒度特征融合的古文命名实体识别方法，以《左传》为研究语料，构建人名、地名、时间等命名实体识别任务。首先，将古文字信息、词性信息及字形特征融合，提高输入特征表示能力；然后，在加入预测实体头尾辅助任务学习古句边界信息的同时利用Transfer交互器启发式学习古文实体构词规律，并用BiLSTM和IDCNN联合抽取上下文信息；最后，将学习到的多种古文特征加权融合，输入CRF中进行实体预测。[结果/结论] 实验结果表明，多粒度特征融合的古文命名实体识别方法，相比主流的BERT-BiLSTM-CRF模型，精确率、召回率和F1值分别提升5.09%、13.45%和9.87%。多粒度特征融合的古文命名实体识别方法能够精准地实现对古籍文本的命名实体识别。
[Purpose/Significance] Leveraging Named Entity Recognition (NER) techniques for the thorough exploration of ancient literary documents not only drives forward the digitization of ancient Chinese texts, including the vital process of Ancient text digitization, which is crucial for historical studies, bolstering cultural confidence, promoting traditional Chinese culture, and advancing Named Entity Recognition (NER) as a foundational task in NLP. [Method/Process] A method for named entity recognition in classical Chinese texts with multi-granularity feature fusion was proposed, Leveraging "Zuo Zhuan" as the research corpus and formulating named entity recognition tasks for personal names, geographical names, temporal entities, etc. Initially, ancient character information, part-of-speech (POS) information, and glyph features were integrated to enhance input feature representation. Subsequently, auxiliary tasks for predicting entity boundaries were introduced, alongside the utilization of a Transfer Interactor heuristic to learn classical Chinese entity formation rules. This was complemented by joint contextual information extraction using BiLSTM and IDCNN (Iterated Dilated Convolutional Neural Network). Finally, learned features were weighted and merged into a CRF (Conditional Random Field) for entity prediction. [Result/Conclusion] Experimental results demonstrate that the proposed method of multi-granularity feature fusion for named entity recognition in classical Chinese texts enhances precision, recall, and F1 score by 5.09%, 13.45%, and 9.87%, respectively, compared to the mainstream BERT-BiLSTM-CRF method. Multi-granularity feature fusion for named entity recognition in classical Chinese texts is crucial for accurately identifying named entities in ancient texts.
digital humanities / classical Chinese / entity recognition / multi-granularity feature fusion
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孟佳娜：设计研究方案，修改论文；
许英傲：提出研究思路，撰写论文；
赵丹丹：采集、清洗和分析数据；
李丰毅：设计实验，处理数据；
赵 迪：修订论文与定稿。
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