A semantic hierarchical graph neural network for text classification
- URL: http://arxiv.org/abs/2209.07031v1
- Date: Thu, 15 Sep 2022 03:59:31 GMT
- Title: A semantic hierarchical graph neural network for text classification
- Authors: Shuai Hua, Xinxin Li, Yunpeng Jing, Qunfeng Liu
- Abstract summary: We propose a new hierarchical graph neural network (HieGNN) which extracts corresponding information from word-level, sentence-level and document-level respectively.
Experimental results on several benchmark datasets achieve better or similar results compared to several baseline methods.
- Score: 1.439766998338892
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The key to the text classification task is language representation and
important information extraction, and there are many related studies. In recent
years, the research on graph neural network (GNN) in text classification has
gradually emerged and shown its advantages, but the existing models mainly
focus on directly inputting words as graph nodes into the GNN models ignoring
the different levels of semantic structure information in the samples. To
address the issue, we propose a new hierarchical graph neural network (HieGNN)
which extracts corresponding information from word-level, sentence-level and
document-level respectively. Experimental results on several benchmark datasets
achieve better or similar results compared to several baseline methods, which
demonstrate that our model is able to obtain more useful information for
classification from samples.
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