This tutorials is part of a three-part series: * `NLP From Scratch: Classifying Names with a Character-Level RNN <https://pytorch.org/tutorials/intermediate/char_rnn ...

Abstract: This review article provides a thorough assessment of modern and innovative algorithms for text classification through both observational and experimental evaluations. We propose a new ...

Since transformer-based language models were introduced in 2017, they have been shown to be extraordinarily effective across a variety of NLP tasks including but not limited to language generation.

In recent decades, medical short texts, such as medical conversations and online medical inquiries, have garnered significant attention and research. The advances in the medical short text have ...

Abstract: We propose a text classification model based on dependency graph attention convolution, which improves the model’s accurate recognition of the text category by mining the semantic features ...

School of Computer Science and Technology, Zhejiang Normal University, Jinhua, China. This study aims to design and implement an efficient news text classification system based on deep learning to ...

It is a universal phenomenon for patients who do not know which clinical department to register in large general hospitals. Although triage nurses can help patients, due to the larger number of ...

There is no model loading and test code in the tutorial, and the way it saves the model seems different with others. Now I think the API used in the tutorial is kind of outdated. The nametuple ...