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A new multifractal-based deep learning model for text mining
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DOI：10.1016/j.ipm.2023.103561.png)
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摘要
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Text mining is indispensable and heavily relied upon in artificial intelligence systems. Nevertheless, a disregard for the complexity of language expression and the challenges like gradient vanishing within prevailing activation functions, shackle the potential of text mining. In this paper, we introduce a new text mining model that deftly marries the concept of fractals to delve into the language's complexity. It employs our proposed multifractal approach to alleviate potential noise, simultaneously harnessing the potency of our proposed activation function to amplify the neural network's performance. The success on experiments anchored in real-world technical reports covering the recognition of technical term and classification of hazard events, stands as a testament to our endeavors. This research venture not only expands our understanding of text mining but also opens new horizons for information processing across various domains.
Keyword:
Text mining
Multifractal analysis
Activation function
Deep learning
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