Graph neural networks for text classification: A survey
Text Classification is the most essential and fundamental problem in Natural Language
Processing. While numerous recent text classification models applied the sequential deep …
Processing. While numerous recent text classification models applied the sequential deep …
BoW-based neural networks vs. cutting-edge models for single-label text classification
To reliably and accurately classify complicated" big" datasets, machine learning models
must be continually improved. This research proposes straightforward yet competitive neural …
must be continually improved. This research proposes straightforward yet competitive neural …
Multi-label text classification based on semantic-sensitive graph convolutional network
D Zeng, E Zha, J Kuang, Y Shen - Knowledge-Based Systems, 2024 - Elsevier
Abstract Multi-Label Text Classification (MLTC) is an important but challenging task in the
field of natural language processing. In this paper, we propose a novel method, Semantic …
field of natural language processing. In this paper, we propose a novel method, Semantic …
DeBERTa-GRU: Sentiment Analysis for Large Language Model.
Modern technological advancements have made social media an essential component of
daily life. Social media allow individuals to share thoughts, emotions, and ideas. Sentiment …
daily life. Social media allow individuals to share thoughts, emotions, and ideas. Sentiment …
G-HFIN: graph-based hierarchical feature integration network for propaganda detection of we-media news articles
In the era of We-media, articles are written by independently individuals that are not officially
registered with the authorities. Propaganda hidden in the We-media articles have the …
registered with the authorities. Propaganda hidden in the We-media articles have the …
Transformers are short-text classifiers
Short text classification is a crucial and challenging aspect of Natural Language Processing.
For this reason, there are numerous highly specialized short text classifiers. A variety of …
For this reason, there are numerous highly specialized short text classifiers. A variety of …
Caselink: Inductive graph learning for legal case retrieval
In case law, the precedents are the relevant cases that are used to support the decisions
made by the judges and the opinions of lawyers towards a given case. This relevance is …
made by the judges and the opinions of lawyers towards a given case. This relevance is …
SES: Bridging the Gap Between Explainability and Prediction of Graph Neural Networks
Despite the Graph Neural Networks'(GNNs) pro-ficiency in analyzing graph data, achieving
high-accuracy and interpretable predictions remains challenging. Existing GNN interpreters …
high-accuracy and interpretable predictions remains challenging. Existing GNN interpreters …
Text classification on heterogeneous information network via enhanced GCN and knowledge
H Li, Y Yan, S Wang, J Liu, Y Cui - Neural Computing and Applications, 2023 - Springer
Graph convolutional networks-based text classification methods have shown impressive
success in further improving the classification results by considering the structural …
success in further improving the classification results by considering the structural …
A text classification method based on LSTM and graph attention network
H Wang, F Li - Connection Science, 2022 - Taylor & Francis
Text classification is a popular research topic in the natural language processing. Recently
solving text classification problems with graph neural network (GNN) has received …
solving text classification problems with graph neural network (GNN) has received …