New Framework Uses Cross-Source Reasoning to Improve Author Name Disambiguation in Academic Databases
Researchers have developed CrossND, a framework that improves author name disambiguation by identifying and correcting inconsistent author-paper assignments across different academic sources. The approach uses data refinement, probabilistic reasoning, and test-time scaling without requiring expert annotation. This matters because accurate author disambiguation is essential for academic search systems and research attribution.
A new paper on arXiv presents CrossND, a full-stack framework designed to address the persistent problem of author name disambiguation in academic search systems. Rather than relying on expert annotation or starting from scratch, the framework leverages inconsistencies in how different sources assign papers to authors as a signal for correction. The approach combines three main components: a chain-of-refinement pipeline that denoises author profiles and improves paper-author matching probabilities, a supervised fine-tuning process with probabilistic soft logic for cross-correction, and test-time scaling for enhanced robustness. Experiments on real-world datasets show that CrossND outperforms 17 baseline methods. This work addresses a critical challenge in academic information systems where cumulative errors in paper-author assignments can propagate through databases and harm research discoverability and attribution.
What's missing
The paper does not specify which academic sources were used in the experiments, the size of the datasets tested, or provide details on the computational cost and scalability of the approach for very large academic databases.
What different sources said
- arXiv cs.CLCenter
Cross-Source Reasoning-based Correction for Author Name Disambiguation
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