The use of ensemble learning, deep learning, and effective document representation
methods is currently some of the most common trends to improve the overall accuracy of a …

Over the last decade Deep learning-based models surpasses classical machine learning
models in a variety of text classification tasks. The primary challenge with text classification …

Efficient distributed numerical word representation models (word embeddings) combined
with modern machine learning algorithms have recently yielded considerable improvement …

Word embeddings act as an important component of deep models for providing input
features in downstream language tasks, such as sequence labelling and text classification …

In recent years, convolutional neural networks (CNNs) have gained considerable attention
in text classification because of the remarkable good performance they achieved in various …

The use of word embedding models and deep learning algorithms are currently the most
common and popular trends to enhance the overall performance of a text classification …

Text classification results can be hindered when just the bag-of-words model is used for
representing features, because it ignores word order and senses, which can vary with the …

Deep neural networks have been displaying superior performance over traditional
supervised classifiers in text classification. They learn to extract useful features automatically …

Purpose The immense quantity of available unstructured text documents serve as one of the
largest source of information. Text classification can be an essential task for many purposes …

Text classification has always been an increasingly crucial topic in natural language
processing. Traditional text classification methods based on machine learning have many …