An Extreme Multi-label Text Classification (XMTC) Library Dataset: What if we took "Use of Practical AI in Digital Libraries" seriously?
AI 摘要
发布大规模双语文本分类数据集,用于知识库索引和辅助编目,旨在提升目录编目工作效率。
主要贡献
- 发布大规模双语GND标注数据集
- 提供机器可读的GND分类法
- 提出基于知识库的文本分类方法
方法论
使用GND知识库对目录记录进行标注,构建多标签文本分类数据集,并评估了三个系统。
原文摘要
Subject indexing is vital for discovery but hard to sustain at scale and across languages. We release a large bilingual (English/German) corpus of catalog records annotated with the Integrated Authority File (GND), plus a machine-actionable GND taxonomy. The resource enables ontology-aware multi-label classification, mapping text to authority terms, and agent-assisted cataloging with reproducible, authority-grounded evaluation. We provide a brief statistical profile and qualitative error analyses of three systems. We invite the community to assess not only accuracy but usefulness and transparency, toward authority-anchored AI co-pilots that amplify catalogers' work.