arXiv：cs.AI· Linhai Ma, Ethan F. Wei, Xueqing Peng, Yan Wang, Lingfei Qian, V\'ictor Guti\'errez-Basulto·· 6 小时前
Factorized Hypothesis Search：面向证据到分类体系检索的新方法
Factorized Hypothesis Search for Evidence-to-Taxonomy Retrieval
AI 导读
针对输入仅为间接证据时大型分类体系检索的“检索就绪度差距”，研究者提出 Factorized Hypothesis Search（FHS），在命名语义维度上维护多个部分解释，支持结构化查询渲染、多假设检索与维度级候选验证。
正文
Abstract:Large-taxonomy retrieval often assumes that the input already expresses the target concept. In many settings, however, the input is indirect evidence, such as a table cell whose meaning depends on its row, column, datatype, and context. We call this mismatch the retrieval readiness gap. Our analysis shows that the current index retrieves the target reliably when its semantics are explicit, while raw evidence often leaves it deep in the ranking. We propose Factorized Hypothesis Search (FHS), which maintains multiple partial interpretations over named semantic dimensions. These hypotheses support structured query rendering, multi-hypothesis retrieval, and dimension-level candidate verification. On both financial taxonomy tagging and CodiEsp clinical coding tasks, FHS achieves the best Recall@1, MRR, and final accuracy among the non-oracle methods. Replacing the factorized hypothesis path with a free-text ensemble causes the largest drop in head-ranking performance, while sequential refinement provides no additional gain over FHS's strong parallel first round.
| Comments: | 27 pages, 1 figure, 28 tables |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2608.06614 [cs.CL] |
| (or arXiv:2608.06614v2 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2608.06614 arXiv-issued DOI via DataCite |
Submission history
From: Linhai Ma [view email]
[v1]
Thu, 6 Aug 2026 21:56:56 UTC (128 KB)
[v2]
Tue, 13 Oct 2026 15:04:32 UTC (125 KB)
来源：arXiv：cs.AI · arxiv.org