构建首个代码仓库中科研成果实体识别数据集，助力自动发现研究实现细节。
NERdME: a Named Entity Recognition Dataset for Indexing Research Artifacts in Code Repositories
- 基于200个手动标注的README文件，构建10类实体的标注数据集。
- 超过10,000个实体标记，验证了代码级实体与论文级实体的差异性。
- 适用于研究者、工具开发者，推动科研可复现性与自动化元数据提取。
现有学术信息抽取（SIE）数据集主要聚焦于科学论文，忽视代码仓库中的实现级细节。README 文件描述数据集、源代码等实现级资源，但其自由格式的 Markdown 缺乏语义结构，导致自动信息抽取困难。为填补这一空白，本文提出 NERdME：包含 200 个手动标注的 README 文件，覆盖超过 10,000 个标注片段和 10 种实体类型。使用大语言模型与微调的 Transformer 进行基线实验，揭示了论文级与实现级实体之间的显著差异，表明将 README 中的实体类型纳入 SIE 基准具有重要价值。下游实体链接实验进一步证明，从 README 中提取的实体可支持科研成果发现与元数据整合。
原文摘要 · Abstract (English)
Existing scholarly information extraction (SIE) datasets focus on scientific papers and overlook implementation-level details in code repositories. README files describe datasets, source code, and other implementation-level artifacts, however, their free-form Markdown offers little semantic structure, making automatic information extraction difficult. To address this gap, NERdME is introduced: 200 manually annotated README files with over 10,000 labeled spans and 10 entity types. Baseline results using large language models and fine-tuned transformers show clear differences between paperlevel and implementation-level entities, indicating the value of extending SIE benchmarks with entity types available in README files. A downstream entity-linking experiment was conducted to demonstrate that entities derived from READMEs can support artifact discovery and metadata integration.
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