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Retrieval scheme for cluster-based adaptive information retrieval based on term refinement

机译:基于术语细化的基于集群的自适应信息检索方案

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Abstract: This paper discusses a retrieval scheme for an information retrieval system in which the feedback from a number of users of the system about its performance (global feedback) is stored in the form of clusters called user-oriented clusters. The clusters are described by using the description of its constituent documents. The clusters and queries are represented as vectors and the measure of similarity between them is represented as the cosine of the angle between the two. The clusters are retrieved as per decreasing order of similarity with respect to a query. An important problem that arises in the context of cluster description is the significance of an index term assigned to documents. This problem, called term refinement problem, is formulated and solved. The experimental results of the proposed retrieval scheme are compared with those of the vector space model and the results obtained are encouraging. !18
机译:摘要:本文讨论了一种信息检索系统的检索方案,其中,系统中许多用户对其性能的反馈(全局反馈)以称为用户导向型集群的集群形式存储。通过使用其组成文档的描述来描述集群。聚类和查询表示为向量,它们之间的相似性度量表示为两者之间的角度的余弦。按照关于查询的相似性递减顺序检索聚类。在群集描述的上下文中出现的一个重要问题是分配给文档的索引项的重要性。制定并解决了这个称为术语优化问题的问题。将所提出的检索方案的实验结果与向量空间模型的实验结果进行了比较,获得的结果令人鼓舞。 !18

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