精简数据融合：以最少相关性标注释放线性组合的威力
Streamlined Data Fusion: Unleashing the Power of Linear Combination with Minimal Relevance Judgments
AI总结：
本文研究在信息检索数据融合中，仅用20%-50%相关文档训练线性组合权重即可接近TREC官方qrels的效果，显著降低人工标注成本。
摘要：
线性组合是信息检索任务中一种强大的数据融合方法，因为它能够针对不同场景调整权重。然而，实现最优权重训练通常需要对大量文档进行人工相关性标注，这是一个劳动密集且成本高昂的过程。在本研究中，我们探究了仅使用20%-50%的相关文档即可获得接近最优权重的可行性。通过在四个TREC数据集上的实验，我们发现使用这一缩减后的集合进行多元线性回归训练得到的权重，与使用TREC官方“qrels”获得的权重几乎不相上下。我们的发现为更高效、更经济的数据融合开辟了潜力，使研究人员和实践者能够以显著更少的投入充分享受其全部益处。
英文摘要：
Linear combination is a potent data fusion method in information retrieval tasks, thanks to its ability to adjust weights for diverse scenarios. However, achieving optimal weight training has traditionally required manual relevance judgments on a large percentage of documents, a labor-intensive and expensive process. In this study, we investigate the feasibility of obtaining near-optimal weights using a mere 20\%-50\% of relevant documents. Through experiments on four TREC datasets, we find that weights trained with multiple linear regression using this reduced set closely rival those obtained with TREC's official "qrels." Our findings unlock the potential for more efficient and affordable data fusion, empowering researchers and practitioners to reap its full benefits with significantly less effort.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
