In the era of deep learning, modeling for most natural language processing (NLP) tasks has
converged into several mainstream paradigms. For example, we usually adopt the …

Textual language is the most natural carrier of human emotion. In natural language
processing, textual emotion recognition (TER) has become an important topic due to its …

Text classification is the most fundamental and essential task in natural language
processing. The last decade has seen a surge of research in this area due to the …

We present, to our knowledge, the first application of BERT to document classification. A few
characteristics of the task might lead one to think that BERT is not the most appropriate …

Text classification is the most fundamental and essential task in natural language
processing. The last decade has seen a surge of research in this area due to the …

Extreme multi-label text classification (XMTC) is an important problem in the era of {\it big
data}, for tagging a given text with the most relevant multiple labels from an extremely large …

This paper presents, TextConvoNet, a novel Convolutional Neural Network (CNN) based
architecture for binary and multi-class text classification problems. Most of the existing CNN …

Hierarchical text classification is an important yet challenging task due to the complex
structure of the label hierarchy. Existing methods ignore the semantic relationship between …

Multi-label text classification (MLTC) aims to tag most relevant labels for the given document.
In this paper, we propose a Label-Specific Attention Network (LSAN) to learn a label-specific …

There is a growing trend to design hybrid neural networks (HNNs) by combining spiking
neural networks and artificial neural networks to leverage the strengths of both. Here, we …