Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Graph Convolutional Network for Swahili News Classification   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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The categories of this paper are: stat
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The categories of this paper are: nucl-th
5
The categories of this paper are: gr-qc
27
The categories of this paper are: comp-gas
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The categories of this paper are: q-fin
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The categories of this paper are: plasm-ph
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The categories of this paper are: math-ph
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The categories of this paper are: hep-ex
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The categories of this paper are: bayes-an
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The categories of this paper are: chao-dyn
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The categories of this paper are: q-bio
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The categories of this paper are: astro-ph
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The categories of this paper are: q-alg
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The categories of this paper are: ao-sci
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The categories of this paper are: solv-int
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The categories of this paper are: funct-an