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arXiv · 2603.05750

NERdME: a Named Entity Recognition Dataset for Indexing Research Artifacts in Code Repositories

Genet Asefa Gesese·Zongxiong Chen·Shufan Jiang·Mary Ann Tan·Zhaotai Liu·Sonja Schimmler·Harald Sack
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Abstract

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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Genet Asefa Gesese, Zongxiong Chen, Shufan Jiang, Mary Ann Tan, Zhaotai Liu, Sonja Schimmler, Harald Sack. 2026-03-05. NERdME: a Named Entity Recognition Dataset for Indexing Research Artifacts in Code Repositories. https://doi.org/10.1145/3774904.3792934

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