Text documents are often represented as high-dimensional and sparse vectors using words as features in a multi-dimensional space. These vectors require a large number of computer resources and it is difficult to capture underlying concepts referred to by the terms. In this paper, we propose to use the technique of dimensionality reduction using Concept Vectors Based on PDDP as a way of solving these problems in the vector space information retrieval model. We give experimental results of the dimensionality reduction by using this method and show that this method is an improvement over conventional vector space model.
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