Document classification, a fundamental process within the field of natural language
processing, has benefitted from the recent advancements in deep learning, particularly in …

The text classification task has a wide range of application domains for distinct purposes,
such as the classification of articles, social media posts, and sentiments. As a natural …

This research paper delves into the realm of deep text clustering algorithms with the aim of
enhancing the accuracy of document classification. In recent years, the fusion of deep …

The proposed methodology for the task of text classification involves the utilization of a deep
learning algorithm that integrates the characteristics of a fusion model. The present model is …

While machines can discover semantic relationships in natural written language, they
depend on human intervention for the provision of the necessary parameters. Precise and …

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 …

Deep learning models such as convolutional neural networks and recurrent networks are
widely applied in text classification. In spite of their great success, most deep learning …

Document representation is central to modern natural language processing systems
including document clustering. Empirical experiments in recent studies provide strong …

ABSTRACT Doc2Sent2Vec is an unsupervised approach to learn lowdimensional feature
vector (or embedding) for a document. This embedding captures the semantics of the …

Traditional text document classification methods represent documents with non-
contextualized word embeddings and vector space models. Recent techniques for text …