Jul 9, 2021 · In this paper, we propose to use graph attention network for word embeddings. We build a large single word graph for a corpus based on word ...

Jul 19, 2021 · In this paper, we propose to use graph attention network for word embeddings. We build a large single word graph for a corpus based on word ...

Oct 20, 2023 · Graph attention networks (GATs) can learn from graph-structured data, such as social networks, citation networks, or knowledge graphs.

Graph Attention Networks for Entity Summarization is the model that applies deep learning on graphs and ensemble learning on entity summarization tasks.

Jul 8, 2024 · A novel network model is proposed for text classification based on Graph Attention Networks (GATs) and sentence-transformer embeddings.

Mar 21, 2023 · This blog post explores the use of graph neural networks to improve word embeddings by taking into account the relationships between data points.

Graph embedding methods represent nodes in a continuous vector space, preserving different types of relational information from the graph.

In this paper, we propose to use graph attention network for word embeddings. We build a large single word graph for a corpus based on word order, then learn a ...

A graphical embedding of all documents is formed through use of beta-skeleton graphs. Node features and edge features between nodes are presented, they contain ...

This paper proposes a deep learning-driven framework designed to enhance the effectiveness of text classification models.