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 Fuzzy Graph Convolutional Network Model for Sentence-Level Sentiment Analysis
Various methods have been developed to improve the performance of sentence-level
sentiment analysis (SLSA), the newest as graph convolutional networks (GCNs), with …
sentiment analysis (SLSA), the newest as graph convolutional networks (GCNs), with …
On enhancement of text classification and analysis of text emotions using graph machine learning and ensemble learning methods on non-english datasets
In recent years, machine learning approaches, in particular graph learning methods, have
achieved great results in the field of natural language processing, in particular text …
achieved great results in the field of natural language processing, in particular text …
FeDN2: Fuzzy-Enhanced Deep Neural Networks for Improvement of Sentence-Level Sentiment Analysis
Sentence-level sentiment analysis is a natural language processing model growing rapidly
and strongly due to its role in artificial intelligence systems. There are many approaches to …
and strongly due to its role in artificial intelligence systems. There are many approaches to …
MM-EMOG: Multi-Label Emotion Graph Representation for Mental Health Classification on Social Media
More than 80% of people who commit suicide disclose their intention to do so on social
media. The main information we can use in social media is user-generated posts, since …
media. The main information we can use in social media is user-generated posts, since …
Graph convolution for large-scale graph node classification task based on spatial and frequency domain fusion
J Lu, L Zheng, X Hua, Y Wang - IEEE Access, 2025 - ieeexplore.ieee.org
In recent years, Graph Neural Networks (GNNs) have achieved significant success in graph-
based tasks. However, they still face challenges in complex scenarios, particularly in …
based tasks. However, they still face challenges in complex scenarios, particularly in …
HGBL: A Fine Granular Hierarchical Multi-Label Text Classification Model
C Zhang, L Dai, C Liu, L Zhang - Neural Processing Letters, 2024 - Springer
Hierarchical multi-label text classification is vital for natural language processing (NLP).
However, existing research rarely makes full use of the interaction between labels and text …
However, existing research rarely makes full use of the interaction between labels and text …
Multimodal Commonsense Knowledge Distillation for Visual Question Answering
Existing Multimodal Large Language Models (MLLMs) and Visual Language Pretrained
Models (VLPMs) have shown remarkable performances in the general Visual Question …
Models (VLPMs) have shown remarkable performances in the general Visual Question …
Text classification method based on dependency parsing and hybrid neural network
X He, S Liu, G Yan, X Zhang - Intelligent Data Analysis, 2024 - journals.sagepub.com
Due to the vigorous development of big data, news topic text classification has received
extensive attention, and the accuracy of news topic text classification and the semantic …
extensive attention, and the accuracy of news topic text classification and the semantic …
A Hybrid GCN and BiLSTM model for news text classification
S Liu, X He, G Yan, X Zhang - Sixth International Conference …, 2023 - spiedigitallibrary.org
News text classification is an important subtask in natural language processing. News text is
not only unstructured text, but also its structure information is relatively fuzzy. Therefore …
not only unstructured text, but also its structure information is relatively fuzzy. Therefore …