Abstract
Nowadays, Graph Convolution Networks (GCNs) have flourished in the field of text classification, such as Text Graph Convolution Network (TextGCN). But good performance of those methods is based on building a graph whose nodes consist of an entire corpus, making their models transductive. Meanwhile rich label information has not been utilized in the graph structure. In this paper, we propose a new model named Inductive Light Graph Convolution Networks (ILGCN) with a new construction of graph. This approach uses labels and words to build the graph which removes the dependence between an individual text and entire corpus, and let ILGCN inductive. Besides, we simplify the model structure and only remain the neighborhood aggregation, which is the most important part of GCNs. Experiments on multiple benchmark show that our model outperforms existing state-of-the-art models on several text classification datasets.
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Acknowledgment
This work is supported by National Key Research and Development Project (2018YFE0119700), Key Research and Development Project of Shandong Province (2019JZZY010132, 2019-0101), the Natural Foundation of Shandong Province (ZR2018MF003), Plan of Youth Innovation Team Development of colleges and universities in Shandong Province (SD2019-161).
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Shi, J., Wu, X., Liu, X., Lu, W., Li, S. (2022). Inductive Light Graph Convolution Network for Text Classification Based on Word-Label Graph. In: Shi, Z., Zucker, JD., An, B. (eds) Intelligent Information Processing XI. IIP 2022. IFIP Advances in Information and Communication Technology, vol 643. Springer, Cham. https://doi.org/10.1007/978-3-031-03948-5_4
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DOI: https://doi.org/10.1007/978-3-031-03948-5_4
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