A New Multifractal-based Deep Learning Model for Text Mining

TL;DR

This paper introduces a novel multifractal-based deep learning model for text mining that captures complex patterns in text data, utilizing a new activation function and demonstrating effectiveness on real-world technical reports.

Contribution

It presents a new multifractal analysis method combined with a specialized activation function within a deep learning framework for improved text mining performance.

Findings

Effective extraction of technical terms from reports

Accurate classification of hazard events

Enhanced understanding of text complexity

Abstract

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.