Impact of word embedding models on text analytics in deep learning environment: a review
The selection of word embedding and deep learning models for better outcomes is vital.
Word embeddings are an n-dimensional distributed representation of a text that attempts to …
Word embeddings are an n-dimensional distributed representation of a text that attempts to …
Multimodal learning with graphs
Artificial intelligence for graphs has achieved remarkable success in modelling complex
systems, ranging from dynamic networks in biology to interacting particle systems in physics …
systems, ranging from dynamic networks in biology to interacting particle systems in physics …
Graph neural networks: foundation, frontiers and applications
The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …
recent years. Graph neural networks, also known as deep learning on graphs, graph …
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 …
Graph neural networks for natural language processing: A survey
Deep learning has become the dominant approach in addressing various tasks in Natural
Language Processing (NLP). Although text inputs are typically represented as a sequence …
Language Processing (NLP). Although text inputs are typically represented as a sequence …
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 …
TAGNN: Target attentive graph neural networks for session-based recommendation
Session-based recommendation nowadays plays a vital role in many websites, which aims
to predict users' actions based on anonymous sessions. There have emerged many studies …
to predict users' actions based on anonymous sessions. There have emerged many studies …
Learning latent relations for temporal knowledge graph reasoning
Abstract Temporal Knowledge Graph (TKG) reasoning aims to predict future facts based on
historical data. However, due to the limitations in construction tools and data sources, many …
historical data. However, due to the limitations in construction tools and data sources, many …
[HTML][HTML] Hierarchical graph-based text classification framework with contextual node embedding and BERT-based dynamic fusion
A Onan - Journal of king saud university-computer and …, 2023 - Elsevier
We propose a novel hierarchical graph-based text classification framework that leverages
the power of contextual node embedding and BERT-based dynamic fusion to capture the …
the power of contextual node embedding and BERT-based dynamic fusion to capture the …
Evidence-aware fake news detection with graph neural networks
The prevalence and perniciousness of fake news has been a critical issue on the Internet,
which stimulates the development of automatic fake news detection in turn. In this paper, we …
which stimulates the development of automatic fake news detection in turn. In this paper, we …