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 …

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 …

In recent years, Active Learning (AL) has been applied in the domain of text classification
successfully. However, traditional methods need researchers to pay attention to feature …

Text classification is a basic task in natural language processing. Recently, pre-training
models such as BERT have achieved outstanding results compared with previous methods …

In practical classification, there is often a mix of learnable and unlearnable classes and only
a classifier above a minimum performance threshold can be deployed. This problem is …

Huge amounts of text data are produced every day. Processing text data that accumulates
and grows exponentially every day requires the use of appropriate automation tools. Text …

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 …

Text classification is an important task in natural language processing. Previous text
classification models do not perform well on short texts due to the data sparsity problem. In …

Text classification is an important task in natural language processing with wide
applications. Traditional text classification methods manually extract the features which are …

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 …