Summary of Text Classification Optimization Algorithm Based on Graph Neural Network, by Erdi Gao et al.
Text classification optimization algorithm based on graph neural network
by Erdi Gao, Haowei Yang, Dan Sun, Haohao Xia, Yuhan Ma, Yuanjing Zhu
First submitted to arxiv on: 9 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper addresses a fundamental task in natural language processing (NLP), text classification, which has significant research value and practical applications. Traditional methods rely on feature representations like bag of words or TF-IDF, overlooking semantic connections between words. Recent advancements in graph neural networks (GNNs) have shown promise for text classification tasks due to their ability to handle non-Euclidean data efficiently. However, existing GNN-based approaches face challenges such as complex graph structure construction and high training costs. This paper proposes a novel optimization algorithm utilizing GNNs, incorporating an adaptive graph construction strategy and efficient graph convolution operation. Experimental results demonstrate the proposed method’s superior performance and efficiency across multiple public datasets, outperforming traditional approaches and existing GNN models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper is about improving how computers understand text. Right now, computers can only read text in a simple way, but humans understand much more. The researchers want to make computers better at understanding text by using something called graph neural networks (GNNs). GNNs are like special maps that help computers see the relationships between words. They tested their new approach on several big datasets and found that it works really well! This is important because it could be used in many applications, such as helping computers understand what people say online. |
Keywords
» Artificial intelligence » Bag of words » Gnn » Natural language processing » Nlp » Optimization » Text classification » Tf idf
