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In this study, we propose a double-phase neural-network-based metalearning approach to perform distributed text information retrieval. In the first phase, a single neural network model is deployed in different text collections distributed in different physical sites to retrieve some relevant text documents. In the second phase, a neural-network-based metalearning approach is proposed to integrate the relevance results for text documents with a specific query. For illustration purpose, a simulated web text information retrieval experiment is performed to verify the effectiveness and efficiency of the proposed neural-network-based metalearning approach.
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