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This investigation, focusing on classification performance, compares the classic BERT and several hybrid versions (i.e., employing different training strategies and approximate Support Vector Machines ...
The growing volume of unstructured text data in the banking sector has created a need for advanced classification methods to manage customer inquiries efficiently, resulting in faster response times, ...
The relentless progression of advanced technologies has driven the seamless integration of Internet of Things (IoT) services into the fundamental framework of contemporary tourism enterprises. In the ...
Arabic documents are massively rising due to numerous contents utilized in websites, social media, and news articles. The classification of such documents in labelled categories is a significant and ...
Text classification is a critical task for understanding the knowledge behind text, especially in medical text. In this paper, we propose a medical graph diffusion model, named the MGD model, for the ...
Chinese text classification is an important task in data mining, which extracts category features from unstructured contents. Conventional Chinese text classification models only leverage the surface ...
Although graph neural networks based methods can solve the uneven text length problem of text classification datasets, they are difficult to address the data sparsity problem of short texts. Although ...
This review article provides a thorough assessment of modern and innovative algorithms for text classification through both observational and experimental evaluations. We propose a new classification ...
This repository contains my implementation of a feed-forward neural network classifier in Python and Keras, trained on the Fashion-MNIST dataset. It closely follows the tutorial by The Clever ...
By employing weakly supervised short-text classification methods, we aim to classify patients’ free-text comments into these predefined themes, providing a more versatile and transferable solution.
Text classification is one of the most important and typical tasks in Natural Language Processing (NLP) which can be applied for many applications. Recently, deep learning approaches has shown their ...
Aiming at the traditional methods of text classification, the dimensions need to be reduced, the features are extracted manually, and the classification accuracy is poor, furthermore, convolutional ...