.png){kind=icon}
.png){kind=icon}
Return
Graph transformer networks based text representation
.png){kind=icon}
PRE
AI
DOI：10.1016/j.neucom.2021.08.032.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Graph Neural Networks (GNN) has been used to exploit global features in text representation learning for natural language processing (NLP) tasks, including text classification, sequence tagging, neural machine translation and relational reasoning. However, GNN based models usually build a graph for the entire cor-pus, they have high memory consumption, ignoring the order of words and containing test documents in the training graph. Thus, these models are inherently transductive and have difficulties in inductive learning. In order to solve the above problems, we propose a Graph Transformer Networks based Text representation (GTNT) model. It first constructs a degree-centric text graph, which generates a text graph for each document in the corpus. Then it adopts a graph transformer network to model the graph to obtain node embeddings. When we apply our proposed GTNT model to citation recommendation and text classification tasks, the experimental results show that our model outperforms other state-of-the-art models. (c) 2021 Elsevier B.V. All rights reserved.
Keywords:
Graph Neural Networks
Graph Transformer Networks
Text representation
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
6.5
Papers:
2.5W
Citations:
6.5W