发表机构
School of Computing, National University of Singapore（新加坡国立大学计算机学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对大概念集分类法归纳性能退化问题，提出SPARROW框架，采用结构保持谱分区与约束引导合并，解决结构碎片化和父节点错位，在大型基准上取得最优全局结构质量。
AI 中文摘要
分类法归纳旨在将概念集组织成连贯的层级结构。近期基于大语言模型的方法可以直接从扁平术语列表归纳出分类法，避免了对语料库的需求，但随着概念集规模的扩大，其性能会急剧下降。我们认为，这种退化不仅源于上下文长度限制，还源于层级推理中的结构性失败。为解决这一问题，我们采用了一种分而治之再合并的范式，将概念划分为更小的子集，归纳局部分类法，并将其合并为全局层级结构。然而，我们识别出该范式固有的两种结构性失败模式：结构碎片化（分区削弱了局部层级信号）和父节点错位（局部看似合理的关系在全局层级中被错误放置）。为同时解决这两个问题，我们提出了SPARROW，一个可扩展的分类法归纳框架，它结合了结构保持的谱分区以保留每个块内的层级连通性，以及约束引导的增量融合，将块级关系视为全局放置的结构约束而非事实依据。在大型基准上的实验表明，SPARROW在各种骨干网络上始终取得最强的全局结构质量。代码可在以下网址获取：此https URL。
英文摘要
Taxonomy induction aims to organize concept sets into coherent hierarchical structures. Recent LLM-based methods can induce taxonomies directly from flat term lists, avoiding the need for corpora, but degrade sharply as concept sets scale up. We argue that this degradation stems not only from context length limitations, but also from structural failures in hierarchical reasoning. To address this, we adopt a divide-and-merge paradigm that partitions concepts into smaller subsets, induces local taxonomies, and merges them into a global hierarchy. However, we identify two structural failure modes inherent to this paradigm: Structural Fragmentation, where partitioning weakens local hierarchical signals, and Parent Displacement, where locally plausible relations are misplaced in the global hierarchy. To address both, we propose SPARROW, a scalable taxonomy induction framework that combines structure-preserving spectral partitioning to retain hierarchical connectivity within each block, and constraint-guided incremental fusion that treats block-level relations as structural constraints rather than ground truth for global placement. Experiments on large-scale benchmarks show that SPARROW consistently achieves the strongest global structural quality across backbones. The code is available at https://github.com/rebeccazyr/SPARROW.
CommentsAccepted to EMNLP 2026 Main
