BoW-based neural networks vs. cutting-edge models for single-label text classification
Source of Publication
Neural Computing and Applications
To reliably and accurately classify complicated "big" datasets, machine learning models must be continually improved. This research proposes straightforward yet competitive neural networks for text classification, even though graph neural networks (GNN) have reignited interest in graph-based text classification models. Convolutional neural networks (CNN), artificial neural networks (ANN), and their refined “fine-tuned” models (denoted as FT-CNN and FT-ANN) are the names given to our proposed models. The models presented in this paper demonstrate that our simple models like (CNN, ANN, FT-CNN, and FT-ANN) can perform better than more complex GNN ones such as (SGC, SSGC, and TextGCN) and are comparable to others (i.e., HyperGAT and Bert). The process of fine-tuning is also highly recommended because it improves the performance and reliability of models. The performance of our suggested models on five benchmark datasets (namely, Reuters (R8), R52, 20NewsGroup, Ohsumed, and Mr) is vividly illustrated. According to the experimental findings, on the majority of the target datasets, these models—especially those that have been fine-tuned—perform surprisingly better than SOTA approaches, including GNN-based models.
Springer Science and Business Media LLC
Data mining, Machine learning, Neural networks, Text classification
Abdalla, Hassan I.; Amer, Ali A.; and Ravana, Sri Devi, "BoW-based neural networks vs. cutting-edge models for single-label text classification" (2023). All Works. 5921.
Indexed in Scopus