The human mind grows in learning new knowledge, which finally organizes and develops a basic mental pattern called cognitive structure. Hierarchical multi-label text classification (HMLTC), a fundamental but challenging task in many real-world applications, aims to classify the documents with hierarchical labels to form a resembling cognitive structure learning process. Existing approaches for HMLTC mainly focus on partial new knowledge learning or the global cognitive-structure-like label structure utilization in a cognitive view. However, the complete cognitive structure learning model is a unity that is indispensably constructed by the global label structure utilization and partial knowledge learning, which is ignored among those HMLTC approaches. To address this problem, we will imitate the cognitive structure learning process into the HMLTC learning and propose a unified framework called Hierarchical Cognitive Structure Learning Model (HCSM) in this paper. HCSM is composed of the Attentional Ordered Recurrent Neural Network (AORNN) submodule and Hierarchical Bi-Directional Capsule (HBiCaps) submodule. Both submodules utilize the partial new knowledge and global hierarchical label structure comprehensively for the HMLTC task. On the one hand, AORNN extracts the semantic vector as partial new knowledge from the original text by the word-level and hierarchy-level embedding granularities. On the other hand, AORNN builds the hierarchical text representation learning corresponding to the global label structure by the document-level neurons ordering. HBiCaps employs an iteration to form a unified label categorization process similar to cognitive-structure learning: firstly, using the probability computation of local hierarchical relationships to maintain partial knowledge learning; secondly, modifying the global hierarchical label structure based on the dynamic routing mechanism between capsules. Moreover, the experimental results on four benchmark datasets demonstrate that HCSM outperforms or matches state-of-the-art text classification methods.

人类的思维在学习新知识中成长，并最终组织并发展出一种称为认知结构的基本心理模式。分层多标签文本分类（HMLTC）是许多实际应用中的一项基本但具有挑战性的任务，旨在通过分层标签对文档进行分类，以形成类似于认知结构的学习过程。HMLTC的现有方法主要集中在部分新知识学习或在认知观点中使用类似于全局认知结构的标签结构。但是，完整的认知结构学习模型是一个整体，它是通过全局标签结构利用和部分知识学习必不可少的一个整体，而在那些HMLTC方法中却被忽略了。为了解决这个问题，我们将在HMLTC学习中模仿认知结构学习过程，并在本文中提出一个称为层次认知结构学习模型（HCSM）的统一框架。HCSM由注意力有序递归神经网络（AORNN）子模块和分层双向胶囊（HBiCaps）子模块组成。两个子模块都将部分新知识和全局分层标签结构全面用于HMLTC任务。一方面，AORNN通过词级和层次级的嵌入粒度从原始文本中提取语义向量作为部分新知识。另一方面，AORNN通过文档级神经元排序来构建与全局标签结构相对应的分层文本表示学习。HBiCaps使用迭代来形成类似于认知结构学习的统一标签分类过程：首先，使用局部层次关系的概率计算来保持部分知识学习。其次，基于胶囊之间的动态路由机制，对全局分层标签结构进行了修改。此外，在四个基准数据集上的实验结果表明，HCSM的性能优于或匹配了最新的文本分类方法。