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.

这项工作实证了文本图卷积网络在半监督 Swahili 新闻分类任务上优于传统自然语言处理基准的能力，特别是针对少标记语料库这一实际限制的实验。此外，作者还提出了一种变体 Text GCN 模型，其利用词袋表示法代替朴素的独热编码方式以减少内存占用，同时展示了类似的预测性能。

用于斯瓦希里语新闻分类的图卷积网络

Graph Convolutional Network for Swahili News Classification

Text classification is an important and classical problem in natural language
processing. There have been a number of studies that applied convolutional
neural networks (convolution on regular grid, e.g., sequence) to
classification. However, only a limited number of studies have explored the
more flexible graph convolutional neural networks (convolution on non-grid,
e.g., arbitrary graph) for the task. In this work, we propose to use graph
convolutional networks for text classification. We build a single text graph
for a corpus based on word co-occurrence and document word relations, then
learn a Text Graph Convolutional Network (Text GCN) for the corpus. Our Text
GCN is initialized with one-hot representation for word and document, it then
jointly learns the embeddings for both words and documents, as supervised by
the known class labels for documents. Our experimental results on multiple
benchmark datasets demonstrate that a vanilla Text GCN without any external
word embeddings or knowledge outperforms state-of-the-art methods for text
classification. On the other hand, Text GCN also learns predictive word and
document embeddings. In addition, experimental results show that the
improvement of Text GCN over state-of-the-art comparison methods become more
prominent as we lower the percentage of training data, suggesting the
robustness of Text GCN to less training data in text classification.

本文介绍了一种基于图卷积神经网络的文本分类算法 (Text GCN)，该算法利用单一的文本图构建并联合学习词和文本的表征，经实验表明，将该算法应用于多个基准数据集后，在无需外部词嵌入和预先知识的情况下，文本 GCN 仍可优于现有的文本分类方法。