Scholar Name Disambiguation with Search-enhanced LLM Across Language
Renyu Zhao, Yunxin Chen

TL;DR
This paper introduces a search-enhanced, multilingual LLM-based approach for scholar name disambiguation, significantly improving accuracy by leveraging search engine capabilities and cross-language features.
Contribution
It presents a novel method combining search engine techniques with large language models to enhance scholar name disambiguation across multiple languages.
Findings
Improved disambiguation accuracy with local language data
Effective use of search engine capabilities for data enrichment
Enhanced performance across diverse geographic regions
Abstract
The task of scholar name disambiguation is crucial in various real-world scenarios, including bibliometric-based candidate evaluation for awards, application material anti-fraud measures, and more. Despite significant advancements, current methods face limitations due to the complexity of heterogeneous data, often necessitating extensive human intervention. This paper proposes a novel approach by leveraging search-enhanced language models across multiple languages to improve name disambiguation. By utilizing the powerful query rewriting, intent recognition, and data indexing capabilities of search engines, our method can gather richer information for distinguishing between entities and extracting profiles, resulting in a more comprehensive data dimension. Given the strong cross-language capabilities of large language models(LLMs), optimizing enhanced retrieval methods with this…
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Taxonomy
TopicsData Quality and Management · Biomedical Text Mining and Ontologies · Library Science and Information Systems
