cwl_eval: An Evaluation Tool for Information Retrieval

L. Azzopardi, Paul Thomas, Alistair Moffat
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引用次数: 22

Abstract

We present a tool ("cwl_eval") which unifies many metrics typically used to evaluate information retrieval systems using test collections. In the CWL framework metrics are specified via a single function which can be used to derive a number of related measurements: Expected Utility per item, Expected Total Utility, Expected Cost per item, Expected Total Cost, and Expected Depth. The CWL framework brings together several independent approaches for measuring the quality of a ranked list, and provides a coherent user model-based framework for developing measures based on utility (gain) and cost. Here we outline the CWL measurement framework; describe the cwl_eval architecture; and provide examples of how to use it. We provide implementations of a number of recent metrics, including Time Biased Gain, U-Measure, Bejewelled Measure, and the Information Foraging Based Measure, as well as previous metrics such as Precision, Average Precision, Discounted Cumulative Gain, Rank-Biased Precision, and INST. By providing state-of-the-art and traditional metrics within the same framework, we promote a standardised approach to evaluating search effectiveness.
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cwl_eval:一个信息检索的评估工具
我们提出了一个工具(“cwl_eval”),它统一了许多通常用于使用测试集合评估信息检索系统的指标。在CWL框架中,度量是通过单个函数指定的,该函数可用于派生出许多相关度量:每个项目的预期效用、预期总效用、每个项目的预期成本、预期总成本和预期深度。CWL框架汇集了几种独立的方法来衡量排名列表的质量,并提供了一个基于用户模型的一致框架,用于开发基于效用(收益)和成本的度量。在这里,我们概述了CWL的测量框架;描述cwl_eval架构;并提供如何使用它的例子。我们提供了许多最新指标的实现,包括时间偏差增益、u型测量、宝石迷阵测量和基于信息采集的测量,以及以前的指标,如精度、平均精度、折扣累积增益、秩偏差精度和INST。通过在同一框架内提供最先进和传统的指标,我们促进了一种评估搜索有效性的标准化方法。
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