Hierarchical multi-label academic text classification (HMTC) is to assign
academic texts into a hierarchically structured labeling system. We propose an
attention-based hierarchical multi-label classification algorithm of academic
texts (AHMCA) by integrating features such as text, keywords, and hierarchical
structure, the academic documents are classified into the most relevant
categories. We utilize word2vec and BiLSTM to obtain embedding and latent
vector representations of text, keywords, and hierarchies. We use hierarchical
attention mechanism to capture the associations between keywords, label
hierarchies, and text word vectors to generate hierarchical-specific document
embedding vectors to replace the original text embeddings in HMCN-F. The
experimental results on the academic text dataset demonstrate the effectiveness
of the AHMCA algorithm.

本文提出了一种基于注意力机制的层次化多标签学术文本分类算法，该算法结合了文本、关键词和层次结构等特征，利用 word2vec 和 BiLSTM 来获取文本、关键词和层次结构的嵌入式和潜在向量表示，并使用分层注意机制来捕捉关键词、标签层次结构和文本词向量之间的关联，以生成特定于分层的文档嵌入向量取代 HMCN-F 中的原始文本嵌入。实验结果显示，该算法具有较好的效果。