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.
@article{arxiv.2603.05750,
title = {NERdME: a Named Entity Recognition Dataset for Indexing Research Artifacts in Code Repositories},
author = {Genet Asefa Gesese and Zongxiong Chen and Shufan Jiang and Mary Ann Tan and Zhaotai Liu and Sonja Schimmler and Harald Sack},
journal= {arXiv preprint arXiv:2603.05750},
year = {2026}
}