Text classification is one of the key problems in natural language processing (NLP), and in
early years, it was usually accomplished by feature-based machine learning models …

Recent advances in recurrent neural nets (RNNs) have shown much promise in many
applications in natural language processing. For most of these tasks, such as sentiment …

In recent years, the exponential growth of digital documents has been met by rapid progress
in text classification techniques. Newly proposed machine learning algorithms leverage the …

In recent years, recurrent neural networks have been widely used for various text
classification tasks. However, most of the recurrent architectures will not assign a class label …

Deep learning is an effective method for extracting the underlying information in text.
However, it performs better on closed datasets and is less effective in real-world scenarios …

Deep learning algorithms achieve exceptional accuracies in various tasks. Despite this
success, those models are known to be prone to errors, ie low in robustness, due to …

Text classification is a foundational task in many NLP applications. Traditional text classifiers
often rely on many human-designed features, such as dictionaries, knowledge bases and …

Text classification is one of the fundamental tasks in natural language processing. Recently,
deep neural networks have achieved promising performance in the text classification task …

The performance of text classification methods has improved greatly over the last decade for
text instances of less than 512 tokens. This limit has been adopted by most state-of-the …

Text classification is a primary task in natural language processing (NLP). Recently, graph
neural networks (GNNs) have developed rapidly and been applied to text classification …