Comparison of Text Classification Algorithms based on Deep Learning

Ping Qu; Beibei Zhang; Jiawei Wu; Hao Yan

doi:10.5281/zenodo.12601298

Authors

Ping Qu Maharishi International University
Beibei Zhang Xi'an Jiaotong University
Jiawei Wu Illinois Institute of Technology
Hao Yan Syracuse University

DOI:

https://doi.org/10.5281/zenodo.12601298

ARK:

https://n2t.net/ark:/40704/JCTAM.v1n2a05

PURL:

https://purl.archive.org/suas/JCTAM.v1n2a05

References:

49

Keywords:

Text Classification, Hyperbolic Space, Graph Attention Network, Deep Learning

Abstract

In the technical battlefield of text classification, extracting key features and solving the sparsity problem play a decisive role in improving the performance of classification results. Euclidean geometric models often distort the processed vectors because they are difficult to deal with complex data structures. This exploration uses hyperbolic space with huge storage potential and hierarchical structure, and proposes an innovative hyperbolic graph-based short text classification technology - L-HGAT, aiming to improve the efficiency of processing concise information. This method combines two technologies, hyperbolic geometry and attention network, to optimize the representation of text through in-depth interaction between labels and text features. The research results significantly show that L-HGAT not only has high accuracy and excellent efficiency in many benchmark data sets, but also effectively integrates label information, significantly enhancing the model's ability to capture local features. This discussion brings an innovative perspective to processing hierarchical information and demonstrates the effectiveness of hyperbolic geometry in text classification challenges.

Author Biographies

Ping Qu, Maharishi International University

Computer Science, Maharishi International University, Fairfield, IA, USA.

Beibei Zhang, Xi'an Jiaotong University

Software Engineering, Xi'an Jiaotong University, Xi'an, China.

Jiawei Wu, Illinois Institute of Technology

Engineering in Artificial Intelligence for Computer Vision and Control, Illinois Institute of Technology, Chicago, IL, USA.

Hao Yan, Syracuse University

Engineering and Computer Science, Syracuse University, Syracuse, NY, USA.

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