Deep learning--based text classification: a comprehensive review
Deep learning--based models have surpassed classical machine learning--based
approaches in various text classification tasks, including sentiment analysis, news …
approaches in various text classification tasks, including sentiment analysis, news …
Review of graph neural network in text classification
Text classification is one of the fundamental problems in Natural Language Processing
(NLP). Several research studies have used deep learning approaches such as Convolution …
(NLP). Several research studies have used deep learning approaches such as Convolution …
A survey on text classification: From traditional to deep learning
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 …
processing. The last decade has seen a surge of research in this area due to the …
Masked label prediction: Unified message passing model for semi-supervised classification
Graph neural network (GNN) and label propagation algorithm (LPA) are both message
passing algorithms, which have achieved superior performance in semi-supervised …
passing algorithms, which have achieved superior performance in semi-supervised …
Text classification via large language models
Despite the remarkable success of large-scale Language Models (LLMs) such as GPT-3,
their performances still significantly underperform fine-tuned models in the task of text …
their performances still significantly underperform fine-tuned models in the task of text …
Graph convolutional networks for text classification
Text classification is an important and classical problem in natural language processing.
There have been a number of studies that applied convolutional neural networks …
There have been a number of studies that applied convolutional neural networks …
Be more with less: Hypergraph attention networks for inductive text classification
Text classification is a critical research topic with broad applications in natural language
processing. Recently, graph neural networks (GNNs) have received increasing attention in …
processing. Recently, graph neural networks (GNNs) have received increasing attention in …
A survey on text classification: From shallow to deep learning
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 …
processing. The last decade has seen a surge of research in this area due to the …
Few-shot named entity recognition: An empirical baseline study
This paper presents an empirical study to efficiently build named entity recognition (NER)
systems when a small amount of in-domain labeled data is available. Based upon recent …
systems when a small amount of in-domain labeled data is available. Based upon recent …
A label attention model for ICD coding from clinical text
ICD coding is a process of assigning the International Classification of Disease diagnosis
codes to clinical/medical notes documented by health professionals (eg clinicians). This …
codes to clinical/medical notes documented by health professionals (eg clinicians). This …