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格式版本: 2
标题: "TEAMMix: Taxonomy Enrichment Augmentation and Minority-augmented Mixing Strategy for LLM-enhanced Weak-Supervised Hierarchical Text Classification"
原文链接: "https://arxiv.org/abs/2608.11044"
发布日期: "2026-08-11"
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发现时间: "2026-08-12T11:16:29+08:00"
入库时间: "2026-08-12T03:19:40.438Z"
来源平台: "arXiv 学术论文搜索"
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AI优质: "否"
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采集批次: "2026年8月12日2点04分20秒"
采集批次ID: "20260812-020420-289"
去重键: "https://arxiv.org/abs/2608.11044"
---

## Computer Science > Computation and Language

## Title:TEAMMix: Taxonomy Enrichment Augmentation and Minority-augmented Mixing Strategy for LLM-enhanced Weak-Supervised Hierarchical Text Classification

[View PDF](https://arxiv.org/pdf/2608.11044) [HTML (experimental)](https://arxiv.org/html/2608.11044v1)

> Abstract:Hierarchical Text Classification (HTC), as a critical text mining task, faces challenges such as complex label hierarchies and class imbalance. Existing methods based on large language models (LLMs) struggle to be efficiently applied to this task due to issues like lengthy prompts and loss of label structural information. To address these limitations, this paper proposes a weakly supervised HTC framework enhanced by LLM-based data augmentation. The framework first enriches the label hierarchy semantically through keyword generation and corpus mining, thereby enhancing the model's understanding of labels. Subsequently, it guides the LLM to generate pseudo-samples to mitigate the long-tail problem, and employs a Gaussian mixture model for confidence-based resampling to optimize the quality of generated data. Experimental results demonstrate that the proposed method effectively improves the reliability of LLM-generated pseudo-labels and significantly enhances classification performance on fine-grained and imbalanced datasets.

| Comments: |  |
| --- | --- |
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | [arXiv:2608.11044](https://arxiv.org/abs/2608.11044) \[cs.CL\] |
|  | (or [arXiv:2608.11044v1](https://arxiv.org/abs/2608.11044v1) \[cs.CL\] for this version) |
|  | [https://doi.org/10.48550/arXiv.2608.11044](https://doi.org/10.48550/arXiv.2608.11044) |
| Related DOI: | [https://doi.org/10.1109/CSCWD68734.2026.11582679](https://doi.org/10.1109/CSCWD68734.2026.11582679) |

## Submission history

From: Jian Zhang \[[view email](https://arxiv.org/show-email/d581cf3b/2608.11044)\]  
**\[v1\]** Tue, 11 Aug 2026 15:16:59 UTC (1,931 KB)

[Which authors of this paper are endorsers?](https://arxiv.org/auth/show-endorsers/2608.11044) | Disable MathJax ([What is MathJax?](https://info.arxiv.org/help/mathjax.html))
