自动构建可评估的多层次文本分类体系，解决大规模反馈整理难题
TaxCE : A Framework for Automated Taxonomy Construction and Evaluation at Scale
- 通过逐级压缩文本内容生成可操作的语义单元和细粒度主题
- 在三个核心指标上分别提升11.8、20.5、15.7个百分点，优于主流方法
- 适合需要高可读性与可导航性的企业级用户反馈分析场景
将非结构化反馈文本组织为层次化分类体系是自然语言处理中的基础挑战，尤其在评论、转录和调查等大规模、多样化数据场景中。现有方法或生成浅层分类，忽略长尾话题，或缺乏严谨评估框架。本文提出TaxCE，一个完全自动化框架，通过逐步压缩语料内容，生成可操作片段、去重语义单元及带定义的细粒度主题，并自底向上构建具有语料根基的层次结构。我们还引入三种基于语料的评估指标：排他性（Exclusivity）、完备性（Exhaustivity）和粒度（Granularity），并集成到迭代优化机制中，诊断缺陷并针对性修正直至收敛。大量实验表明，TaxCE在经典主题模型、神经方法和大模型基线上的平均表现分别提升11.8、20.5和15.7个百分点。人工评估进一步验证其分类质量、可操作性与导航性更优。
原文摘要 · Abstract (English)
Organizing unstructured feedback text into hierarchical taxonomy is a fundamental challenge in NLP, particularly in domains where feedback arrives at massive scale in varied forms such as reviews, transcripts, and surveys. Existing approaches either produce shallow hierarchies, neglect long-tail topics, or lack rigorous evaluation frameworks. We present TaxCE, a fully automated framework that constructs multi-level hierarchical taxonomies from raw text through progressive condensation of corpus content into actionable segments, deduplicated semantic units, and granular topics with definitions, which are then organized bottom-up into a hierarchy with corpus-groundedness. We also introduce three corpus-grounded evaluation metrics, Exclusivity, Exhaustivity, and Granularity (EEG), and integrate them into a metrics-in-the-loop iterative refinement mechanism that diagnoses deficiencies and applies targeted corrections until convergence. Extensive experiments demonstrate that TaxCE consistently outperforms existing baselines spanning classical topic models, neural methods, and LLM-based approaches, with average improvements of 11.8, 20.5, and 15.7 percentage points in exclusivity, exhaustivity, and granularity respectively over the strongest baseline. Human evaluation further confirms superior taxonomy quality, actionability, and navigability.
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