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Extended Language Models for XML Element Retrieval

机译:XML元素检索的扩展语言模型

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In this paper we describe our participation in the INEX 2010 ad-hoc track. We participated in three retrieval tasks (restricted focused task, relevant-in-context, restricted relevant-in-context) and report our findings based on a single set of measure for all tasks. In this year's participation, we evaluate the performance of the standard language model that is more focused on a fixed number of relevant characters than on relevant paragraphs. Our findings are: 1) the simplest language model for document retrieval performs relatively well in the restricted focused task when using a fixed offset that is close to the average character distance from the beginning of a document to its main content; 2) a good result of document ranking does improve the performance of snippet retrieval; 3) stemming and stopword removal can further boost performance.
机译:在本文中,我们描述了我们参与Inex 2010 Ad-hoc轨道。我们参加了三个检索任务(限制了焦点的任务,相关的上下文,限制相关的上下文),并根据所有任务的一组措施向我们的调查结果报告。在今年的参与中,我们评估了标准语言模型的性能,这些模型更加专注于固定数量的相关字符而不是相关段落。我们的研究结果是:1)当使用靠近文档开始到其主要内容的固定偏移时,记录检索的最简单语言模型在受限制的聚焦任务中执行相对较好的偏移。 2)文件排名的良好结果确实改善了片段检索的性能; 3)Stemming和StopWord拆卸可以进一步提高性能。

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