动态抽样满足池化

G. Cormack, Haotian Zhang, Nimesh Ghelani, Mustafa Abualsaud, Mark D. Smucker, Maura R. Grossman, Shahin Rahbariasl, Amira Ghenai
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引用次数: 4

摘要

一个由六名评估人员组成的团队使用动态抽样(Cormack和Grossman 2018)和每个主题一小时的评估工作,形成TREC 2018年共同核心轨道的测试集合,而不是汇集。后来,NIST对深度-10池选择的通过移动到前端(MTF)池(Cormack et al. 1998)增强的文档以及我们的动态采样工作选择的文档进行了官方相关性评估。使用xinfAP统计评估器从动态采样评估中呈现的MAP估计与使用标准tre_eval工具从完整的官方评估集呈现的MAP估计相当。另一方面,仅使用池选择的文档呈现的MAP估计有很大不同。结果表明,使用没有池化的动态抽样可以在一个数量级上减少评估工作,产生具有更低偏差、更低误差的信息检索有效性估计,并具有对系统有效性排序的可比较能力。
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Dynamic Sampling Meets Pooling
A team of six assessors used Dynamic Sampling (Cormack and Grossman 2018) and one hour of assessment effort per topic to form, without pooling, a test collection for the TREC 2018 Common Core Track. Later, official relevance assessments were rendered by NIST for documents selected by depth-10 pooling augmented by move-to-front (MTF) pooling (Cormack et al. 1998), as well as the documents selected by our Dynamic Sampling effort. MAP estimates rendered from dynamically sampled assessments using the xinfAP statistical evaluator are comparable to those rendered from the complete set of official assessments using the standard trec_eval tool. MAP estimates rendered using only documents selected by pooling, on the other hand, differ substantially. The results suggest that the use of Dynamic Sampling without pooling can, for an order of magnitude less assessment effort, yield information-retrieval effectiveness estimates that exhibit lower bias, lower error, and comparable ability to rank system effectiveness.
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