Multi-label classification is an extension of multi-class classification. For multi-label problem, each instance may not be restricted to have only one label. In this paper, the methods to solve multi ...

The deep learning-based method is also a supervised approach, training neural network classification models on text data with emotion labels, and utilizing the strong fitting ability of neural ...

The methodology introduces a batch-mode active learning framework for deep multi-label text classification. Starting with a small labeled dataset, the framework iteratively selects unlabeled samples ...

Multi-label text categorization is a crucial task in Natural Language Processing, where each text instance can be simultaneously assigned to numerous labels. In this research, our goal is to assess ...

In this paper, we use BigEarthNet-MM (Sumbul et al., 2021) dataset for both self-supervised training and classification task evaluation, which is the most common multi-label scene classification ...

Because of the easy usage, flexible configuration for all text tasks, and the support for multi-task learning, this codebase is especially useful for running non-contextual baselines for text ...