David Lillis, F. Toolan, Rem W. Collier, J. Dunnion
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引用次数: 96
摘要
数据融合是将文档集合上的独立搜索结果组合到一个输出结果集中。过去已经证明,这可以大大提高检索效率,而不是单个结果。提出了一种基于概率的数据融合方法probFuse。ProbFuse假设单个输入系统在许多训练查询上的表现表明了它们未来的表现。融合的结果集是基于在训练过程中计算的相关概率。利用TREC ad hoc数据集进行的检索实验表明,probFuse取得的结果优于流行的CombMNZ融合算法。
Data fusion is the combination of the results of independent searches on a document collection into one single output result set. It has been shown in the past that this can greatly improve retrieval effectiveness over that of the individual results.This paper presents probFuse, a probabilistic approach to data fusion. ProbFuse assumes that the performance of the individual input systems on a number of training queries is indicative of their future performance. The fused result set is based on probabilities of relevance calculated during this training process. Retrieval experiments using data from the TREC ad hoc collection demonstrate that probFuse achieves results superior to that of the popular CombMNZ fusion algorithm.