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HeteroHTC: Enhancing hierarchical text classification via Heterogeneity Encoding of Label Hierarchy

EXPERT SYSTEMS WITH APPLICATIONS 2025年 271卷

作者： Song, Junru Chen, Tianlei Yang, Yang Wang, Feifei Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai 200240 Peoples R China Renmin Univ China Ctr Appl Stat Beijing 100872 Peoples R China Renmin Univ China Sch Stat Beijing 100872 Peoples R China Peking Univ Sch Comp Sci Beijing 100072 Peoples R China

hierarchical text classification (HTC) is a challenging subtask of multi-label text classification, where labels are organized into a pre-defined hierarchy. Recent works primarily encode documents and labels separately before cross attention-based feature extraction, and in the process, they collectively overlook a crucial characteristic of label hierarchies: "heterogeneity". Specifically, labels on different levels hold different granularities, and they should be projected onto distinct feature spaces;The relationships among labels are various, dictating that the message transmission among them should occur in unique feature spaces. We term these properties ''granularity heterogeneity"and "relationship heterogeneity", respectively. To fully exploit these ubiquitous yet overlooked properties, we propose HeteroHTC, which features a heterogeneous label hierarchy encoder. Additionally, we leverage pre-trained Large Language Models (LLMs) to generate high-quality label descriptions with strategically designed prompts. HeteroHTC outperforms almost all baselines in our extensive experiments on three datasets, proving its effectiveness and the necessity to take "granularity and relationship heterogeneity"into consideration.

关键词： hierarchical text classification Heterogeneous Graph Transformer Large Language Models

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Disentangled feature graph for hierarchical text classification

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INFORMATION PROCESSING & MANAGEMENT 2025年第3期62卷

作者： Liu, Renyuan Zhang, Xuejie Wang, Jin Zhou, Xiaobing Yunnan Univ Sch Informat Sci & Engn Kunming 650500 Yunnan Peoples R China

Effectively utilizing the hierarchical relationship among labels is the core of hierarchical text classification (HTC). Previous research on HTC has tended to enhance the dependencies between labels. However, they overlook some labels that may conflict with other labels because alleviating label conflicts also weakens label dependencies and reduces the model performance. Therefore, this paper focuses on the issue of label conflicts and studies methods to alleviate label conflicts without affecting the mutual support relationship between labels. To solve the abovementioned problem, we first use the feature disentanglement method to cut off all label connections. Then, the connection among labels is selectively established by constructing a hierarchical graph on disentangled features. Finally, the Graph Neural Networks (GNN) is adopted to encode the obtained Disentanglement Feature Graph (DFG) and enables only labels with connections to support each other, while labels without connections do not interfere with each other. The experimental results on the WOS, RCV1-v2, and BGC datasets show the effectiveness of DFG. In detail, the experimental results show that on the WOS dataset, the model incorporating DFG achieved a 1.07% improvement in Macro-F1, surpassing the best model by 0.27%. On the RCV1-v2 dataset, the model incorporating DFG achieved a 0.95% improvement in Micro-F1, surpassing the best model by 0.21%. On the BGC dataset, the model incorporating DFG achieved a 1.81% improvement in Micro-F1, surpassing the best model by 0.45%.

关键词： Feature disentanglement hierarchical text classification Task conflicts and dependencies

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JumpLiteGCN: A Lightweight Approach to hierarchical text classification 13th

JumpLiteGCN: A Lightweight Approach to Hierarchical Text Cla...

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13th International Conference on Natural Language Processing and Chinese Computing

作者： Liu, Teng Liu, Xiangzhi Dong, Yunfeng Wu, Xiaoming Qilu Univ Technol Shandong Comp Sci Ctr Key Lab Comp Power Network & Informat Secur Minist EducNatl Supercomp Ctr JinanShandong Aca Jinan Peoples R China Shandong Fundamental Res Ctr Comp Sci Shandong Prov Key Lab Comp Networks Jinan Peoples R China

ISBN: (纸本)9789819794393;9789819794409

hierarchical text classification poses a significant challenge in natural language processing due to its intricate label hierarchy. Existing text classification methods often face dual constraints of efficiency and performance. To overcome these challenges, this study proposes a lightweight graph convolutional network model enhanced with jump connections (JumpLiteGCN). This significantly reduces the model's complexity and computational costs by simplifying the network structure. Moreover, integrating jump connection mechanisms enhances the flow of information in deep networks, better capturing and utilizing hierarchical label information, thus significantly improving classification accuracy. In addition, we propose an adaptive loss function weight calculation method that computes the label weights based on hierarchical relationships and applies them to loss function, enabling the model to focus more on the accurate prediction of important samples during training, further enhancing the model's performance and generalization ability. Extensive experiments conducted on two public hierarchical text classification datasets demonstrate that our method surpasses existing state-of-the-art approaches across multiple key performance metrics while significantly reducing the model's training and inference times.

关键词： Graph convolutional network Jump connections hierarchical text classification

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HTCSI: A hierarchical text classification Method Based on Selection-Inference 13th

HTCSI: A Hierarchical Text Classification Method Based on Se...

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13th International Conference on Natural Language Processing and Chinese Computing

作者： Xu, Yiming Feng, Jianzhou Gu, Chenghan Qin, Haonan Xue, Kehan Yanshan Univ Sch Informat Sci & Engn Qinhuangdao 066000 Hebei Peoples R China

ISBN: (纸本)9789819794331;9789819794348

hierarchical text classification (HTC) is a complex task that involves categorizing text within a hierarchical labeling system. Current methodologies primarily focus on integrating hierarchical information into encoding models to achieve enhanced text representations. However, the intricate nature of the process obtaining representation often diminishes the model's interpretability, leaving researchers uncertain about the model's effective utilization of hierarchical information and the rationale behind its results. In this paper, we introduce a novel hierarchical text classification method based on Selection-Inference(HTCSI) using large language models (LLMs) to enhance interpretability. Specifically, we leverage classification paths within the labeling hierarchy as cue information to guide LLM reasoning. Additionally, we develop a path selection module to identify the most probable classification paths, thereby minimizing the impact of irrelevant information on LLM reasoning. We employ the Parameter-Efficient Fine-Tuning (PEFT) method to bolster the reasoning capabilities of LLMs, thus improving the accuracy of hierarchical text classification while ensuring a more reliable reasoning process. Experiments conducted on several standard datasets demonstrate that HTCSI not only enhances credible classification reasoning but also surpasses other advanced HTC methods.

关键词： hierarchical text classification Large Language Models Chain of Thought

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TELEClass: Taxonomy Enrichment and LLM-Enhanced hierarchical text classification with Minimal Supervision 25

TELEClass: Taxonomy Enrichment and LLM-Enhanced Hierarchical...

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Proceedings of the ACM on Web Conference 2025

作者： Yunyi Zhang Ruozhen Yang Xueqiang Xu Rui Li Jinfeng Xiao Jiaming Shen Jiawei Han University of Illinois Urbana-Champaign Urbana IL USA University of Science and Technology of China Hefei China Google DeepMind New York NY USA

ISBN: (纸本)9798400712746

hierarchical text classification aims to categorize each document into a set of classes in a label taxonomy, which is a fundamental web text mining task with broad applications such as web content analysis and semantic indexing. Most earlier works focus on fully or semi-supervised methods that require a large amount of human annotated data which is costly and time-consuming to acquire. To alleviate human efforts, in this paper, we work on hierarchical text classification with a minimal amount of supervision: using the sole class name of each node as the only supervision. Recently, large language models (LLM) have shown competitive performance on various tasks through zero-shot prompting, but this method performs poorly in the hierarchical setting because it is ineffective to include the large and structured label space in a prompt. On the other hand, previous weakly-supervised hierarchical text classification methods only utilize the raw taxonomy skeleton and ignore the rich information hidden in the text corpus that can serve as additional class-indicative features. To tackle the above challenges, we propose TELEClass, Taxonomy Enrichment and LLM-Enhanced weakly-supervised hierarchical text Classification, which combines the general knowledge of LLMs and task-specific features mined from an unlabeled corpus. TELEClass automatically enriches the raw taxonomy with class-indicative features for better label space understanding and utilizes novel LLM-based data annotation and generation methods specifically tailored for the hierarchical setting. Experiments show that TELEClass can significantly outperform previous baselines while achieving comparable performance to zero-shot prompting of LLMs with drastically less inference cost.

关键词： hierarchical text classification

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hierarchical text classification USING CNNS WITH LOCAL APPROACHES

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COMPUTING AND INFORMATICS 2020年第5期39卷 907-924页

作者： Krendzelak, Milan Jakab, Frantisek Tech Univ Kosice Fac Elect Engn & Informat Dept Comp & Informat Letna 9 Kosice 04001 Slovakia

In this paper, we discuss the application of convolutional neural networks (CNNs) for hierarchical text classification using local top-down approaches. We present experimental results implementing a local classification per node approach, a local classification per parent node approach, and a local classification per level approach. A 20 Newsgroup hierarchical training dataset with more than 20 categories and three hierarchical levels was used to train the models. The experiments involved several variations of hyperparameters settings such as batch size, embedding size, and number of available examples from the training dataset, including two variation of CNN model text embedding such as static (stat) and random (rand). The results demonstrated that our proposed use of CNNs outperformed flat CNN baseline model and both the flat and hierarchical support vector machine (SVM) and logistic regression (LR) baseline models. In particular, hierarchical text classification with CNN-stat models using local per parent node and local per level approaches achieved compelling results and outperformed the former and latter state-of-the-art models. However, using CNN with local per node approach for hierarchical text classification underperformed and achieved worse results. Furthermore, we performed a detailed comparison between the proposed hierarchical local approaches with CNNs. The results indicated that the hierarchical local classification per level approach using the CNN model with static text embedding achieved the best results, surpassing the flat SVM and LR baseline models by 7 % and 13 %, surpassing the flat CNN baseline by 5 %, and surpassing the h-SVM and h-LR models by 5 % and 10 %, respectively.

关键词： hierarchical text classification convolutional neural network local top-down approach

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hierarchical text classification based on LDA and Domain Ontology

Hierarchical Text Classification based on LDA and Domain Ont...

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2nd International Conference on Information Technology and Management Innovation (ICITMI 2013)

作者： An, Wei Liu, Qihua Wuhan Univ Sch Informat Management Wuhan 430072 Peoples R China Jiangxi Univ Finance & Econ Sch Informat Technol Nanchang Jiangxi Peoples R China

This paper combines domain ontology and LDA model to propose a new method of hierarchical web text classification. Experimental results show that the method has good performance with high recall rate and accuracy rate.

ISBN: (纸本)9783037858646

关键词： text classification Latent Dirichlet allocation Domain ontology hierarchical text classification

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hierarchical text classification Incremental Learning

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16th International Conference on Neural Information Processing (ICONIP 2009)

作者： Song, Shengli Qiao, Xiaofei Chen, Ping Xidian Univ Inst Software Engn Xian 710071 Peoples R China

ISBN: (纸本)9783642106767

To classify large-scale text corpora, an incremental learning method for hierarchical text classification is proposed. Based on the deep analysis of virtual classification tree based hierarchical text classification, combining the two application models of single document adjustment after classification and new sample set learning, a dynamic online learning algorithm and a sample set incremental learning algorithm are put forward. By amending classifiers and updating the feature space, the algorithms improve the current classification models. hierarchical text classification experiments on Newsgroup datasets show that the algorithms can enhance the classification accuracy effectively and reduce the storage space and the learning time cost of the history sample datasets.

关键词： machine learning incremental learning hierarchical text classification text mining

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Utilizing global and path information with language modelling for hierarchical text classification

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JOURNAL OF INFORMATION SCIENCE 2014年第2期40卷 127-145页

作者： Oh, Heung-Seon Myaeng, Sung-Hyon Korea Adv Inst Sci & Technol Dept Comp Sci Taejon 305701 South Korea Korea Adv Inst Sci & Technol Div Web Sci & Technol Taejon 305701 South Korea

hierarchical text classification of a Web taxonomy is challenging because it is a very large-scale problem with hundreds of thousands of categories and associated documents. Furthermore, the conceptual levels and training data availabilities of categories vary widely. The narrow-down approach is the state of the art;it utilizes a search engine for generating candidates from the taxonomy and builds a classifier for the final category selection. In this paper, we take the same approach but address the issue of using global information in a language modelling framework to improve effectiveness. We propose three methods of using non-local information for the task: a passive way of utilizing global information for smoothing;an aggressive way where a top-level classifier is built and integrated with a local model;and a method of using label terms associated with the path from a category to the root, which is based on our systematic observation that they are underrepresented in the documents. For evaluation, we constructed a document collection from Web pages in the Open Directory Project. A series of experiments and their results show the superiority of our methods and reveal the role of global information in hierarchical text classification.

关键词： language models hierarchical text classification web taxonomy

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An analysis of hierarchical text classification using word embeddings

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INFORMATION SCIENCES 2019年 471卷 216-232页

作者： Stein, Roger Alan Jaques, Patricia A. Valiati, Joao Francisco Univ Vale Rio Sinos UNISINOS Programa Posgrad Comp Aplicada PPGCA Av Unisinos 950 Sao Leopoldo RS Brazil AIE Rua Vieira Castro 262 Porto Alegre RS Brazil

Efficient distributed numerical word representation models (word embeddings) combinec with modern machine learning algorithms have recently yielded considerable improvement on automatic document classification tasks. However, the effectiveness of such techniques has not been assessed for the hierarchical text classification (HTC) yet. This stud investigates the application of those models and algorithms on this specific problem b3 means of experimentation and analysis. We trained classification models with prominent machine learning algorithm implementations-fasttext, XGBoost, SVM, and Keras' CNN-and noticeable word embeddings generation methods-GloVe, word2vec, and fasttextwith publicly available data and evaluated them with measures specifically appropriate fot the hierarchical context. Fasttext achieved an LcAF(1) of 0.893 on a single-labeled version o the RCV1 dataset. An analysis indicates that using word embeddings and its flavors is very promising approach for HTC. (C) 2018 Elsevier Inc. All rights reserved

关键词： hierarchical text classification Word embeddings Gradient tree boosting fasttext Support vector machines

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