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Metric Indexing for the Vector Model in Text Retrieval

机译:文本检索中的矢量模型的度量索引

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In the area of Text Retrieval, processing a query in the vector model has been verified to be qualitatively more effective than searching in the boolean model. However, in case of the classic vector model the current methods of processing many-term queries are inefficient, in case of LSI model there does not exist an efficient method for processing even the few-term queries. In this paper we propose a method of vector query processing based on metric indexing, which is efficient especially for the LSI model. In addition, we propose a concept of approximate semi-metric search, which can further improve the efficiency of retrieval process. Results of experiments made on moderate text collection are included.
机译:在文本检索的区域中,在向量模型中处理查询已经被验证,而不是在布尔模型中搜索的定性更有效。然而,在经典矢量模型的情况下,在LSI模型的情况下,在LSI模型的情况下,当前处理许多术语查询的方法效率低效,即使是几术语查询也不存在有效的处理方法。在本文中,我们提出了一种基于度量索引的向量查询处理方法,这对于LSI模型特别是高效的。此外,我们提出了一种近似半公制搜索的概念,这可以进一步提高检索过程的效率。包括在中等文本收集的实验结果。

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