AI RESEARCH
TabEmb: Joint Semantic-Structure Embedding for Table Annotation
arXiv CS.LG
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ArXi:2604.18939v1 Announce Type: new Table annotation is crucial for making web and enterprise tables usable in downstream NLP applications. Unlike textual data where learning semantically rich token or sentence embeddings often suffice, tables are structured combinations of columns wherein useful representations must jointly capture column's semantics and the inter-column relationships. Existing models learn by linearizing the 2D table into a 1D token sequence and encoding it with pretrained language models (PLMs) such as.