| Texto completo | |
| Autor(es): |
dos Santos, Nicolas Roque
;
Minatel, Diego
;
Baria Valejo, Alan Demetrius
;
Lopes, Alneu de A.
Número total de Autores: 4
|
| Tipo de documento: | Artigo Científico |
| Fonte: | PROGRESS IN PATTERN RECOGNITION, IMAGE ANALYSIS, COMPUTER VISION, AND APPLICATIONS, CIARP 2023, PT I; v. 14469, p. 16-pg., 2024-01-01. |
| Resumo | |
Text classification is a fundamental task in Text Mining (TM) with applications ranging from spam detection to sentiment analysis. One of the current approaches to this task is Graph Neural Network (GNN), primarily used to deal with complex and unstructured data. However, the scalability of GNNs is a significant challenge when dealing with large-scale graphs. Multilevel optimization is prominent among the methods proposed to tackle the issues that arise in such a scenario. This approach uses a hierarchical coarsening technique to reduce a graph, then applies a target algorithm to the coarsest graph and projects the output back to the original graph. Here, we propose a novel approach for text classification using GNN. We build a bipartite graph from the input corpus and then apply the coarsening technique of the multilevel optimization to generate ten contracted graphs to analyze the GNN's performance, training time, and memory consumption as the graph is gradually reduced. Although we conducted experiments on text classification, we emphasize that the proposed method is not bound to a specific task and, thus, can be generalized to different problems modeled as bipartite graphs. Experiments on datasets from various domains and sizes show that our approach reduces memory consumption and training time without significantly losing performance. (AU) | |
| Processo FAPESP: | 21/06210-3 - Serviços cientes dos espaços urbanos via federated learning em sistemas de transporte inteligente |
| Beneficiário: | Geraldo Pereira Rocha Filho |
| Modalidade de apoio: | Auxílio à Pesquisa - Regular |
| Processo FAPESP: | 22/03090-0 - Análise de grandes volumes de dados políticos e redes complexas: mineração, modelagens e aplicações em Ciência Política Computacional |
| Beneficiário: | Sylvia Iasulaitis |
| Modalidade de apoio: | Auxílio à Pesquisa - Projeto Inicial |
| Processo FAPESP: | 22/09091-8 - Criminalidade, insegurança e legitimidade: uma abordagem transdisciplinar |
| Beneficiário: | Luis Gustavo Nonato |
| Modalidade de apoio: | Auxílio à Pesquisa - Programa eScience e Data Science - Temático |
| Processo FAPESP: | 20/09835-1 - IARA - Inteligência Artificial Recriando Ambientes |
| Beneficiário: | André Carlos Ponce de Leon Ferreira de Carvalho |
| Modalidade de apoio: | Auxílio à Pesquisa - Programa Centros de Pesquisa em Engenharia |