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Graph Convolutional Network for Swahili News Classification

arXiv 2103.09325 ↗First posted 16 Mar 2021cs.CL cs.LGcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 32 of its 57 references that are in the map. First posted on arXiv 16 Mar 2021.

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32 of 57 references.

References · 32

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20 more
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