发表机构
University of Michigan; University of Pennsylvania（密歇根大学; 宾夕法尼亚大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究通过固定配对分类器，发现改进多语言公司名称检索虽大幅提升配对召回，但聚类增益有限，因多数新配对低于决策阈值，强调编码器评估需关注最终聚类质量。
AI 中文摘要
当配对分类器保持不变时，改进的名称检索可能对公司聚类几乎没有影响。我们通过在固定候选预算和下游决策规则下适配多语言E5编码器来检验这种依赖关系。随机负样本和困难负样本训练使用相同的正样本调度。在收集新的GLEIF样本（包含3,633个名称、2,880个源身份和882个银标准正配对）之前选择检查点。在72,660条候选边中，使用多视图选择器的适配将直接配对召回率从53.74%提升至76.98%。主匹配器仅增加了七个正确的和两个错误的共聚类配对：聚类召回率从32.54%上升至33.33%，而精确率从95.99%下降至95.45%。在208个新检索到的银标准正配对中，202个低于其决策阈值。随机负样本和困难负样本训练产生相同的最终分区。一项由AI辅助、单人审查的137个配对审计支持观察到的模式，尽管其主要基于LEI的证据并未建立独立的金标准标签。结果将检索增益的即时损失定位在现有的确认阶段，并表明编码器评估还必须衡量最终的聚类质量。
英文摘要
Improved name retrieval may have little effect on company clusters when the pair classifier remains unchanged. We examine this dependency by adapting multilingual E5 encoders under fixed candidate budgets and downstream decision rules. Random-negative and hard-negative training use identical positive schedules. Checkpoints are selected before collecting a new GLEIF sample of 3,633 names, 2,880 source identities and 882 silver-positive pairs. At 72,660 candidate edges, adaptation with a multi-view selector increases direct pair recall from 53.74% to 76.98%. The primary matcher adds only seven correct and two incorrect co-cluster pairs: cluster recall rises from 32.54% to 33.33%, while precision falls from 95.99% to 95.45%. Of 208 newly retrieved silver-positive pairs, 202 fall below its decision threshold. Random-negative and hard-negative training produce identical final partitions. An AI-assisted, single-reviewer audit of 137 pairs supports the observed pattern, although its predominantly LEI-derived evidence does not establish independent gold labels. The results locate the immediate loss of retrieval gains at the existing confirmation stage and show why encoder evaluation must also measure final cluster quality.
