AI framework developed to track dataset usage in research literature

Researchers have developed a new AI framework to track and classify dataset usage within academic literature, addressing a gap in current research infrastructure. This multitask GLiNER-based system jointly extracts dataset mentions, identifies relations, and classifies usage contexts. To overcome the challenge of limited labeled data, the methodology incorporates synthetic data generation and LLM-based revalidation to improve accuracy and consistency in monitoring dataset citations. AI

IMPACT Enhances transparency and reproducibility in research by enabling better tracking of dataset citations.

RANK_REASON The cluster contains an academic paper detailing a new methodology and framework for monitoring dataset usage in research literature. [lever_c_demoted from research: ic=1 ai=1.0]

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• arXiv
• GLiNER
• Google Scholar
• Hugging Face
• Semantic Scholar

• paper
• product

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