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Relevance feedback revisited: dealing with content and structure in XML documents

机译:回顾相关性反馈:处理XML文档中的内容和结构

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摘要

Relevance feedback (RF) is a technique that allows to enrich an initial query according to the user feedback. The goal is to express more precisely the user's needs. Some open issues arise when considering semi-structured documents like XML documents. They are mainly related to the form of XML documents which mix content and structure information and to the new granularity of information. Indeed, the main objective of XML retrieval is to select relevant elements in XML documents instead of whole documents. Most of the RF approaches proposed in XML retrieval are simple adaptation of traditional RF to the new granularity of information. They usually enrich queries by adding terms extracted from relevant elements instead of terms extracted from whole documents. In this article, we describe a new approach of RF that takes advantage of two sources of evidence: the content and the structure. We propose to use the query term proximity to select terms to be added to the initial query and to use generic structures to express structural constraints. Both sources of evidence are used in different combined forms. Experiments were carried out within the INEX evaluation campaign and results show the effectiveness of our approaches.
机译:相关性反馈(RF)是一种允许根据用户反馈来丰富初始查询的技术。目的是更精确地表达用户的需求。在考虑半结构化文档(如XML文档)时会出现一些未解决的问题。它们主要与混合内容和结构信息的XML文档的形式以及新的信息粒度有关。实际上,XML检索的主要目标是选择XML文档中的相关元素,而不是整个文档。 XML检索中提出的大多数RF方法都是将传统RF轻松适应新的信息粒度。他们通常通过添加从相关元素中提取的术语而不是从整个文档中提取的术语来丰富查询。在本文中,我们描述了一种利用两种证据来源的RF的新方法:内容和结构。我们建议使用查询词接近度来选择要添加到初始查询中的词,并使用通用结构来表达结构约束。两种证据来源均以不同的组合形式使用。在INEX评估活动中进行了实验,结果表明了我们方法的有效性。

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