Deep learning has been widely used in natural language processing (NLP) such as
document classification. For example, self-attention has achieved significant improvement in …

Deep neural networks have been widely used in text classification. However, it is hard to
interpret the neural models due to the complicate mechanisms. In this work, we study the …

Document classification has a broad application in the field of sentiment classification,
document ranking and topic labeling, etc. Previous neural network-based work has mainly …

In China, the government documents are documents with legal effect and of standard forms
formulated in the process of government administration. With the continuous development of …

Document classification is one of the foundational tasks of Natural Language Processing
(NLP) applications. In this paper, we introduce an attention based hierarchical recurrent and …

Predictive Coding in legal document review, also called Text Categorization in machine
learning, has been widely used in the legal industry. By leveraging machine learning …

Deep neural networks have greatly improved natural language processing and text analysis
technologies. In particular, pre-trained large language models have achieved significant …

The traditional text classification methods are based on machine learning. It requires a large
amount of artificially labeled training data as well as human participation. However, it is …

Existing text classification models based on graph convolutional networks usually update
node representations simply by fusing neighborhood information of different orders through …

Research of document classification is ongoing to employ the attention based-deep learning
algorithms and achieves impressive results. Owing to the complexity of the document …