arXiv cs.CL· Noah Dasanaike·· 2 天前AI 评分10
EnsembleLink：无需训练数据的高精度记录链接方法
EnsembleLink: Accurate Record Linkage Without Training Data
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EnsembleLink 是一种无需任何训练标签即可实现高精度记录链接的方法，利用预训练语言模型从大规模文本语料中学到的语义关系进行匹配。在涵盖城市名、人名、机构、多语言政党及书目记录的基准上，其表现达到或超过需要大量标注的方法。该方法可在开源模型上本地运行，无需外部 API 调用，典型链接任务几分钟内即可完成。
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Abstract:Record linkage, the process of matching records that refer to the same entity across datasets, is essential to empirical social science but remains methodologically underdeveloped. Researchers treat it as a preprocessing step, applying ad hoc rules without quantifying the uncertainty that linkage errors introduce into downstream analyses. Existing methods either achieve low accuracy or require substantial labeled training data. I present EnsembleLink, a method that achieves high accuracy without any training labels. EnsembleLink leverages pre-trained language models that have learned semantic relationships (e.g., that "South Ozone Park" is a neighborhood in "New York City" or that "Lutte ouvriere" refers to the Trotskyist "Workers' Struggle" party) from large text corpora. On benchmarks spanning city names, person names, organizations, multilingual political parties, and bibliographic records, EnsembleLink matches or exceeds methods requiring extensive labeling. The method runs locally on open-source models, requiring no external API calls, and completes typical linkage tasks in minutes.
| Comments: | This paper has been superseded by arXiv:2610.19048 (merged from arXiv:2601.21132 and arXiv:2601.21138) |
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2601.21138 [cs.CL] |
| (or arXiv:2601.21138v3 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2601.21138 arXiv-issued DOI via DataCite |
Submission history
From: Noah Dasanaike [view email]
[v1]
Thu, 29 Jan 2026 00:41:11 UTC (14 KB)
[v2]
Fri, 27 Feb 2026 22:46:29 UTC (15 KB)
[v3]
Thu, 15 Oct 2026 01:58:41 UTC (1 KB) (withdrawn)
来源：arXiv cs.CL · arxiv.org