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- W4320717123 abstract "Sentence classification is an important task in natural language processing. The task makes use of deep networks to enclose a mass of features with different granularities in a sentence. However, the classification usually suffer from severe performance degradation when stacking a large number of networks. The main reason is that, in a deep architecture, the silent feature representations are easily weakened and mixed with noisy information, which is not effective in learning contextual features and constructing semantic dependencies in a sentence. In this paper, a deep penetration network (DPN) is designed to improve deep architectures’ ability to preserve the favourable semantic features. The DPN enables salient features to penetrate through a deeper architecture and to construct long semantic dependencies between them. This approach is evaluated on seven public datasets. Our experiments show that the DPN exhibits a stable performance with deeper architectures. It improves the performance on three types of sentence classification tasks, outperforming the existing state-of-the-art models." @default.
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- W4320717123 date "2023-07-01" @default.
- W4320717123 modified "2023-10-13" @default.
- W4320717123 title "A deep penetration network for sentence classification" @default.
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- W4320717123 doi "https://doi.org/10.1016/j.inffus.2023.02.015" @default.
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