A New Multifractal-based Deep Learning Model for Text Mining
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Abstract (arXiv)
In this world full of uncertainty, where the fabric of existence weaves patterns of complexity, multifractal emerges as beacons of insight, illuminating them. As we delve into the realm of text mining that underpins various natural language processing applications and powers a range of intelligent services, we recognize that behind the veil of text lies a manifestation of human thought and cognition, intricately intertwined with the complexities. Building upon the foundation of perceiving text as a complex system, this study embarks on a journey to unravel the hidden treasures within, armed with the proposed multifractal method that deciphers the multifractal attributes embedded within the text landscape. This endeavor culminates in the birth of our novel model, which also harnesses the power of the proposed activation function to facilitate nonlinear information transmission within its neural network architecture. The success on experiments anchored in real-world technical reports covering the extraction 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 knowledge discovery across various domains.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 6 of its 66 references that are in the map. First posted on arXiv 27 Nov 2021.
Builds on
6 of 66 references.
References · 6
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
- 2021Activation Functions in Deep Learning: A Comprehensive Survey and Benchmarkcited by 155
- 2020Deep Learning Based Text Classification: A Comprehensive Reviewcited by 141
- 2021Mask is All You Need: Rethinking Mask R-CNN for Dense and Arbitrary-Shaped Scene Text Detectioncited by 23
- 2018Comparison of non-linear activation functions for deep neural networks on MNIST classification taskcited by 19
- 2020Tag Embedding Based Personalized Point Of Interest Recommendation Systemcited by 2
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Citing papers per year: 2023: 1; 2024: 1.
2 papers.