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首页> 外文会议>British National Conference on Databases(BNCOD 23); 20060718-23; Belfast(GB) >Information Retrieval Evaluation with Partial Relevance Judgment
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Information Retrieval Evaluation with Partial Relevance Judgment

机译：具有部分相关性判断的信息检索评估

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摘要

Mean Average Precision has been widely used by researchers in information retrieval evaluation events such as TREC, and it is believed to be a good system measure because of its sensitivity and reliability. However, its drawbacks as regards partial relevance judgment has been largely ignored. In many cases, partial relevance judgment is probably the only reasonable solution due to the large document collections involved. In this paper, we will address this issue through analysis and experiment. Our investigation shows that when only partial relevance judgment is available, mean average precision suffers from several drawbacks: inaccurate values, no explicit explanation, and being subject to the evaluation environment. Further, mean average precision is not superior to some other measures such as precision at a given document level for sensitivity and reliability, both of which are believed to be the major advantages of mean average precision. Our experiments also suggest that average precision over all documents would be a good measure for such a situation.
机译：研究人员已在研究诸如TREC的信息检索评估事件中广泛使用了平均平均精度，并且由于其灵敏度和可靠性，它被认为是一种很好的系统指标。但是，它在部分相关性判断方面的缺点已被大大忽略。在许多情况下，由于涉及大量文档，因此部分相关性判断可能是唯一合理的解决方案。在本文中，我们将通过分析和实验解决这个问题。我们的研究表明，当只有部分相关性判断可用时，平均平均精度会受到以下缺陷的困扰：值不准确，没有明确的解释以及受评估环境的影响。此外，平均平均精度在灵敏度和可靠性方面不优于其他某些度量（例如，给定文档级别的精度），这两者均被认为是平均平均精度的主要优点。我们的实验还表明，所有文档的平均精度对于这种情况是一个很好的衡量标准。

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