从“相同”到“相似”：基于文档间相似性的检索列表融合
From "Identical" to "Similar": Fusing Retrieved Lists Based on Inter-Document Similarities
摘要：
用于融合响应于查询而检索的文档列表的方法通常利用列表中的文档的检索分数和/或排名。我们提出了一种新的融合方法，该方法是基于使用，此外，从文档间的相似性引起的信息。具体来说，我们的方法让来自不同列表的类似文档相互提供相关性状态支持。我们使用基于图的方法来建模文档之间的相关性状态传播。传播由文档间相似性和列表中文档的检索分数决定。实证评估表明，我们的方法在融合TREC运行的有效性。我们最有效的方法的性能超越了只利用检索分数或排名的有效融合方法。
英文摘要：
Methods for fusing document lists that were retrieved in response to a query often utilize the retrieval scores and/or ranks of documents in the lists. We present a novel fusion approach that is based on using, in addition, information induced from inter-document similarities. Specifically, our methods let similar documents from different lists provide relevance-status support to each other. We use a graph-based method to model relevance-status propagation between documents. The propagation is governed by inter-document-similarities and by retrieval scores of documents in the lists. Empirical evaluation demonstrates the effectiveness of our methods in fusing TREC runs. The performance of our most effective methods transcends that of effective fusion methods that utilize only retrieval scores or ranks.
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。
