构建音乐史实体识别与链接数据集，提升古籍文本实体消歧能力
Musical Heritage Historical Entity Linking
- 提出MHERCL数据集，聚焦音乐史文献中稀有实体
- 无监督模型结合知识图谱约束，显著提升历史实体链接准确率
- 适合研究文化遗产数字化与历史文本理解的学者使用
从音乐领域历史期刊中提取并人工标注句子，构建了名为音乐遗产命名实体识别、分类与链接（MHERCL）的新基准数据集。该数据集包含许多在主流知识库中缺失或代表性不足的历史实体。我们在多个前沿实体链接模型上测试，发现该数据集对所有模型均具挑战性。为此，提出一种新型无监督实体链接模型，并设计基于知识图谱的扩展方法，用于增强有监督链接器。实验表明，结合无监督技术、知识图谱逻辑约束及启发式规则预测未在参考知识库中出现的实体（NIL实体），能有效提升历史文档中的实体链接性能。
原文摘要 · Abstract (English)
Linking named entities occurring in text to their corresponding entity in a Knowledge Base (KB) is challenging, especially when dealing with historical texts. In this work, we introduce Musical Heritage named Entities Recognition, Classification and Linking (MHERCL), a novel benchmark consisting of manually annotated sentences extrapolated from historical periodicals of the music domain. MHERCL contains named entities under-represented or absent in the most famous KBs. We experiment with several State-of-the-Art models on the Entity Linking (EL) task and show that MHERCL is a challenging dataset for all of them. We propose a novel unsupervised EL model and a method to extend supervised entity linkers by using Knowledge Graphs (KGs) to tackle the main difficulties posed by historical documents. Our experiments reveal that relying on unsupervised techniques and improving models with logical constraints based on KGs and heuristics to predict NIL entities (entities not represented in the KB of reference) results in better EL performance on historical documents.
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