Segment-driven Structural Induction and Semantic Alignment for Heterogeneous Tabular Representation

• URL: http://arxiv.org/abs/2606.01890v1
• Date: Mon, 01 Jun 2026 08:34:25 GMT
• Title: Segment-driven Structural Induction and Semantic Alignment for Heterogeneous Tabular Representation
• Abstract summary: NAVI is a segment-centric pretraining framework that treats each header-value pair as the unit for aggregating schema-level structural evidence and column-level distributional evidence.<n>Masked Segment Modeling and Entropy-driven Segment Alignment jointly enforce structured header-value coupling and semantic alignment across stable and instance-specific attributes.
• Score: 11.279399724798383
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Real-world domains often contain heterogeneous tables whose headers vary while their underlying attribute semantics are shared, making it difficult to induce domain-specialized semantics from table-local evidence alone. Existing encoders model parts of this problem, but often underuse column-level value distributions and apply uniform objectives across attributes with different semantic roles. We propose NAVI, a segment-centric pretraining framework that treats each header-value pair as the unit for aggregating schema-level structural evidence and column-level distributional evidence. We realize this design through Masked Segment Modeling and Entropy-driven Segment Alignment, which jointly enforce structured header-value coupling and semantic alignment across stable and instance-specific attributes. Experiments on heterogeneous in-domain tables show improved reconstruction, semantic consistency, and downstream utility across evaluation settings overall.

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