Although graph neural networks based methods can solve the uneven text length problem of text classification datasets, they are difficult to address the data sparsity problem of short texts. Although ...

Hands-on coding of a multiclass neural network from scratch, with softmax and one-hot encoding. #Softmax #MulticlassClassification #PythonAI ...

However, the challenges posed by the short length, professional medical vocabulary, complex medical measures, and feature sparsity are further magnified in medical short text classification compared ...

Existing zero-shot text classification methods based on large pre-trained models with added prompts exhibit strong representational capacity and scalability but have relatively poor commercial ...

Therefore, we present an NLP framework for SML, incorporating four major NLP components: external knowledge bases, i.e., ontologies, few-shot text classification, zero-shot text classification also ...

In recent years, the use of synthetic data has emerged as a practical solution to address these issues, yet generating realistic and useful synthetic datasets has remained a complex task, especially ...