Graph Convolutional Network for Swahili News Classification

• URL: http://arxiv.org/abs/2103.09325v1
• Date: Tue, 16 Mar 2021 21:03:47 GMT
• Title: Graph Convolutional Network for Swahili News Classification
• Authors: Alexandros Kastanos and Tyler Martin
• Abstract summary: This work empirically demonstrates the ability of Text Graph Convolutional Network (Text GCN) to outperform traditional natural language processing benchmarks for the task of semi-supervised Swahili news classification.
• Score: 78.6363825307044
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This work empirically demonstrates the ability of Text Graph Convolutional Network (Text GCN) to outperform traditional natural language processing benchmarks for the task of semi-supervised Swahili news classification. In particular, we focus our experimentation on the sparsely-labelled semi-supervised context which is representative of the practical constraints facing low-resourced African languages. We follow up on this result by introducing a variant of the Text GCN model which utilises a bag of words embedding rather than a naive one-hot encoding to reduce the memory footprint of Text GCN whilst demonstrating similar predictive performance.

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