Abstract: Topical text classification, which involves organizing text based on its subject matter or topics, faces significant challenges such as limited training data, ambiguous boundaries between ...

Abstract: Text classification is one of the central challenges in natural language processing, encompassing techniques for cate-gorizing large amounts of text data into meaningful categories. This ...

Large pre-trained language models have become a crucial backbone for many downstream tasks in natural language processing (NLP), and while they are trained on a plethora of data containing a variety ...

The successful application of large-scale transformer models in Natural Language Processing (NLP) is often hindered by the substantial computational cost and data requirements of full fine-tuning.

NeuralClassifier is designed for quick implementation of neural models for hierarchical multi-label classification task, which is more challenging and common in real-world scenarios. A salient feature ...

ABSTRACT: This study addresses the challenges of data noise and model interpretability in depression diagnosis by proposing an intelligent diagnostic framework based on real-world medical scenarios.

def __init__(self, batches, batch_size, device): self.batch_size = batch_size self.batches = batches self.n_batches = len(batches) // batch_size self.residue = False ...