Query processing in search engines can be optimized for use for all queries. For this, system component parameters such as the weighting function or the automatic query expansion model can be optimized or learned from past queries. However, it may be more interesting to optimize the processing thread on a query-by-query basis by adjusting the component parameters; this is what selective query processing does. Selective query processing uses one of the candidate processing threads chosen at query time. The choice is based on query features. In this paper, we examine selective query processing in different settings, both in terms of effectiveness and efficiency; this includes selective query expansion and other forms of selective query processing (e.g., when the term weighting function varies or when the expansion model varies). We found that the best trade-off between effectiveness and efficiency is obtained when using the best trained processing thread and its query expansion counter part. This seems to be also the most natural for a real-word engine since the two threads use the same core engine (e.g., same term weighting function).
翻译:搜索引擎的查询处理可以优化, 供所有查询使用。 对于这一点, 系统组成部分参数, 如加权功能或自动查询扩展模式等, 可以优化, 或者从过去查询中学习。 但是, 可能更有趣的是, 通过调整组件参数, 在逐个查询的基础上优化处理线; 这就是选择性查询处理的作用。 有选择的查询处理使用在查询时选择的候选处理线之一。 选择基于查询特征。 本文中, 我们检查不同环境中的选择性查询处理, 无论是在有效性和效率方面; 这包括选择性查询扩展和其他形式的选择性查询处理( 例如, 当术语加权功能不同或扩展模式不同时 ) 。 我们发现, 当使用经过最佳训练的处理线及其查询扩展反面时, 效果和效率之间的最佳权衡是获得的。 这似乎也是真实字引擎最自然的, 因为两个线索使用相同的核心引擎( 例如, 相同的术语加权功能 ) 。