KeNet: Knowledge-enhanced Doc-Label Attention Network for Multi-label text classification

Bo Li, Yuyan Chen, Liang Zeng
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Abstract

Multi-Label Text Classification (MLTC) is a fundamental task in the field of Natural Language Processing (NLP) that involves the assignment of multiple labels to a given text. MLTC has gained significant importance and has been widely applied in various domains such as topic recognition, recommendation systems, sentiment analysis, and information retrieval. However, traditional machine learning and Deep neural network have not yet addressed certain issues, such as the fact that some documents are brief but have a large number of labels and how to establish relationships between the labels. It is imperative to additionally acknowledge that the significance of knowledge is substantiated in the realm of MLTC. To address this issue, we provide a novel approach known as Knowledge-enhanced Doc-Label Attention Network (KeNet). Specifically, we design an Attention Network that incorporates external knowledge, label embedding, and a comprehensive attention mechanism. In contrast to conventional methods, we use comprehensive representation of documents, knowledge and labels to predict all labels for each single text. Our approach has been validated by comprehensive research conducted on three multi-label datasets. Experimental results demonstrate that our method outperforms state-of-the-art MLTC method. Additionally, a case study is undertaken to illustrate the practical implementation of KeNet.
KeNet:用于多标签文本分类的知识增强文档标签注意网络
多标签文本分类(MLTC)是自然语言处理(NLP)领域的一项基本任务,涉及给定文本分配多个标签。多标签文本分类具有重要意义,已被广泛应用于主题识别、推荐系统、情感分析和信息检索等多个领域。然而,传统的机器学习和深度神经网络尚未解决某些问题,例如有些文档虽然简短,但却有大量标签,以及如何建立标签之间的关系。此外,还必须承认,知识的重要性在 MLTC 领域得到了证实。为了解决这个问题,我们提供了一种称为知识增强文档标签注意力网络(KeNet)的新方法。具体来说,我们设计的注意力网络融合了外部知识、标签嵌入和综合注意力机制。与传统方法相比,我们使用文档、知识和标签的综合表示来预测每个单一文本的所有标签。我们的方法已在三个多标签数据集上进行了综合研究验证。实验结果表明,我们的方法优于最先进的 MLTC 方法。此外,我们还进行了一项案例研究,以说明 KeNet 的实际应用。
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