Multi-label text classification involves extracting all relevant labels from a sentence. Given the unordered nature of these labels, we propose approaching the problem as a set prediction task. To address the correlation between labels, we leverage Graph Convolutional Networks and construct an adjacency matrix based on the statistical relations between labels. Additionally, we enhance recall ability by applying the Bhattacharyya distance to the output distributions of the set prediction networks. We evaluate the effectiveness of our approach on two multi-label datasets and demonstrate its superiority over previous baselines through experimental results.

翻译：多标签文本分类涉及从句子中提取所有相关标签。鉴于这些标签的无序特性，我们提出将其视为集合预测任务来处理。为解决标签间的相关性，我们利用图卷积网络，并基于标签之间的统计关系构建邻接矩阵。此外，我们通过将 Bhattacharyya 距离应用于集合预测网络的输出分布，增强了召回能力。我们在两个多标签数据集上评估了方法的有效性，并通过实验结果证明了其相较于先前基线模型的优越性。