Text classification using graph convolutional network
Graph Convolutional Networks (GCNs) have emerged as a powerful framework for processing and analysing data represented as graphs, finding applications across various domains such as social networks, biological networks, and recommendation systems. This report provides a literature review specificall...
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sg-ntu-dr.10356-1771592024-05-31T15:43:30Z Text classification using graph convolutional network Koh, Jiahui S Supraja School of Electrical and Electronic Engineering supraja.s@ntu.edu.sg Computer and Information Science Engineering Text classification Graph convolutional network Natural language processing Graph Convolutional Networks (GCNs) have emerged as a powerful framework for processing and analysing data represented as graphs, finding applications across various domains such as social networks, biological networks, and recommendation systems. This report provides a literature review specifically focused on TextGCN, delving into its architecture, applications, and comparative efficacy against other state-of-the-art methods. The effectiveness of TextGCN is assessed through experimentation on IMDb and DBpedia datasets, evaluating its performance across varied data contexts. Additionally, this study explores the utilisation of different activation functions and optimisers, changing the number of hidden layers to enhance the model's capabilities. Bachelor's degree 2024-05-27T06:02:03Z 2024-05-27T06:02:03Z 2024 Final Year Project (FYP) Koh, J. (2024). Text classification using graph convolutional network. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/177159 https://hdl.handle.net/10356/177159 en A3267-231 application/pdf Nanyang Technological University |
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Graph Convolutional Networks (GCNs) have emerged as a powerful framework for processing and analysing data represented as graphs, finding applications across various domains such as social networks, biological networks, and recommendation systems. This report provides a literature review specifically focused on TextGCN, delving into its architecture, applications, and comparative efficacy against other state-of-the-art methods. The effectiveness of TextGCN is assessed through experimentation on IMDb and DBpedia datasets, evaluating its performance across varied data contexts. Additionally, this study explores the utilisation of different activation functions and optimisers, changing the number of hidden layers to enhance the model's capabilities. |
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S Supraja |
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S Supraja Koh, Jiahui |
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Final Year Project |
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Koh, Jiahui |
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Koh, Jiahui |
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Text classification using graph convolutional network |
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Text classification using graph convolutional network |
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Text classification using graph convolutional network |
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Text classification using graph convolutional network |
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Text classification using graph convolutional network |
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text classification using graph convolutional network |
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Nanyang Technological University |
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2024 |
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https://hdl.handle.net/10356/177159 |
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