# Hierarchical Taxonomy-Aware and Attentional Graph Capsule RCNNs for Large-Scale Multi-Label Text Classification

arXiv: https://arxiv.org/abs/1906.04898 · HTML: https://mlsanity.com/paper/1906.04898/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jun 2019.

## Built on by (most cited first, 30 of 32)

- [Incorporating Hierarchy into Text Encoder: a Contrastive Learning Approach for Hierarchical Text Classification](https://mlsanity.com/paper/2203.03825/) (2022)
- [Hyperbolic Variational Graph Neural Network for Modeling Dynamic Graphs](https://mlsanity.com/paper/2104.02228/) (2021)
- [Reinforced Neighborhood Selection Guided Multi-Relational Graph Neural Networks](https://mlsanity.com/paper/2104.07886/) (2021)
- [HTCInfoMax: A Global Model for Hierarchical Text Classification via Information Maximization](https://mlsanity.com/paper/2104.05220/) (2021)
- [Higher-Order Attribute-Enhancing Heterogeneous Graph Neural Networks](https://mlsanity.com/paper/2104.07892/) (2021)
- [HiTIN: Hierarchy-aware Tree Isomorphism Network for Hierarchical Text Classification](https://mlsanity.com/paper/2305.15182/) (2023)
- [Sparse Structure Learning via Graph Neural Networks for Inductive Document Classification](https://mlsanity.com/paper/2112.06386/) (2021)
- [Exploiting Global and Local Hierarchies for Hierarchical Text Classification](https://mlsanity.com/paper/2205.02613/) (2022)
- [Enquire One's Parent and Child Before Decision: Fully Exploit Hierarchical Structure for Self-Supervised Taxonomy Expansion](https://mlsanity.com/paper/2101.11268/) (2021)
- [Graph Entropy Guided Node Embedding Dimension Selection for Graph Neural Networks](https://mlsanity.com/paper/2105.03178/) (2021)
- [Hierarchical Interdisciplinary Topic Detection Model for Research Proposal Classification](https://mlsanity.com/paper/2209.13519/) (2022)
- [Interdisciplinary Fairness in Imbalanced Research Proposal Topic Inference: A Hierarchical Transformer-based Method with Selective Interpolation](https://mlsanity.com/paper/2309.01717/) (2023)
- [Contrastive Document Representation Learning with Graph Attention Networks](https://mlsanity.com/paper/2110.10778/) (2021)
- [Hierarchical Text Classification As Sub-Hierarchy Sequence Generation](https://mlsanity.com/paper/2111.11104/) (2021)
- [HiGen: Hierarchy-Aware Sequence Generation for Hierarchical Text Classification](https://mlsanity.com/paper/2402.01696/) (2024)
- [Learning What You Need from What You Did: Product Taxonomy Expansion with User Behaviors Supervision](https://mlsanity.com/paper/2203.14921/) (2022)
- [Type Information Utilized Event Detection via Multi-Channel GNNs in Electrical Power Systems](https://mlsanity.com/paper/2211.08168/) (2022)
- [AE-smnsMLC: Multi-Label Classification with Semantic Matching and Negative Label Sampling for Product Attribute Value Extraction](https://mlsanity.com/paper/2310.07137/) (2023)
- [Informed deep hierarchical classification: a non-standard analysis inspired approach](https://mlsanity.com/paper/2409.16956/) (2024)
- [A Cascade Dual-Decoder Model for Joint Entity and Relation Extraction](https://mlsanity.com/paper/2106.14163/) (2021)
- [Hierarchy-Aware T5 with Path-Adaptive Mask Mechanism for Hierarchical Text Classification](https://mlsanity.com/paper/2109.08585/) (2021)
- [Are We Really Making Much Progress in Text Classification? A Comparative Review](https://mlsanity.com/paper/2204.03954/) (2022)
- [GANTEE: Generative Adversatial Network for Taxonomy Entering Evaluation](https://mlsanity.com/paper/2303.14480/) (2023)
- [DDNAS: Discretized Differentiable Neural Architecture Search for Text Classification](https://mlsanity.com/paper/2307.06005/) (2023)
- [JPAVE: A Generation and Classification-based Model for Joint Product Attribute Prediction and Value Extraction](https://mlsanity.com/paper/2311.04196/) (2023)
- [Introducing Three New Benchmark Datasets for Hierarchical Text Classification](https://mlsanity.com/paper/2411.19119/) (2024)
- [LH-Mix: Local Hierarchy Correlation Guided Mixup over Hierarchical Prompt Tuning](https://mlsanity.com/paper/2412.16963/) (2024)
- [Hierarchical Text Classification Using Contrastive Learning Informed Path Guided Hierarchy](https://mlsanity.com/paper/2506.04381/) (2025)
- [Combining Language and Topic Models for Hierarchical Text Classification](https://mlsanity.com/paper/2507.16490/) (2025)
- [HELM: Hierarchical and Explicit Label Modeling with Graph Learning for Multi-Label Image Classification](https://mlsanity.com/paper/2603.11783/) (2026)

## Abstract (arXiv)

CNNs, RNNs, GCNs, and CapsNets have shown significant insights in representation learning and are widely used in various text mining tasks such as large-scale multi-label text classification. However, most existing deep models for multi-label text classification consider either the non-consecutive and long-distance semantics or the sequential semantics, but how to consider them both coherently is less studied. In addition, most existing methods treat output labels as independent methods, but ignore the hierarchical relations among them, leading to useful semantic information loss. In this paper, we propose a novel hierarchical taxonomy-aware and attentional graph capsule recurrent CNNs framework for large-scale multi-label text classification. Specifically, we first propose to model each document as a word order preserved graph-of-words and normalize it as a corresponding words-matrix representation which preserves both the non-consecutive, long-distance and local sequential semantics. Then the words-matrix is input to the proposed attentional graph capsule recurrent CNNs for more effectively learning the semantic features. To leverage the hierarchical relations among the class labels, we propose a hierarchical taxonomy embedding method to learn their representations, and define a novel weighted margin loss by incorporating the label representation similarity. Extensive evaluations on three datasets show that our model significantly improves the performance of large-scale multi-label text classification by comparing with state-of-the-art approaches.