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System Performance and Natural Language Expression of Information Needs

机译：信息需求的系统性能和自然语言表达

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摘要

Consider information retrieval systems that respond to a query (a natural language statement of a topic, an information need) with an ordered list of 1000 documents from the document collection. From the responses to queries that all express the same topic, one can discern how the words associated with a topic result in particular system behavior. From what is discerned from different topics, one can hypothesize abstract topic factors that influence system performance. An example of such a factor is the specificity of the topic's primary key word. This paper shows that statements about the effect of abstract topic factors on system performance can be supported empirically. A combination of statistical methods is applied to system responses from NIST's Text REtrieval Conference. We analyze each topic using a measure of irrelevant-document exclusion computed for each response and a measure of dissimilarity between relevant-document return orders computed for each pair of responses. We formulate topic factors through graphical comparison of measurements for different topics. Finally, we propose for each topic a four-dimensional summarization that we use to select topic comparisons likely to depict topic factors clearly.
机译：考虑信息检索系统，该系统使用文档集中的1000个文档的有序列表来响应查询（主题的自然语言陈述，信息需求）。从所有都表达相同主题的查询的响应中，可以辨别与主题关联的单词如何导致特定的系统行为。从不同的主题中可以看出，可以假设一些抽象主题因素会影响系统性能。此类因素的一个示例是主题主关键字的特殊性。本文表明，可以凭经验支持有关抽象主题因素对系统性能的影响的陈述。统计方法的组合被应用于NIST的文本检索会议的系统响应。我们使用针对每个响应计算的不相关文档排除措施以及针对每对响应计算的相关文档退货订单之间差异的度量来分析每个主题。我们通过图形比较不同主题的度量来制定主题因子。最后，我们为每个主题提出一个四维摘要，用于选择可能比较清楚地描述主题因素的主题比较。

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