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TabEmb addresses limitations in table annotation by decoupling semantic encoding from structural modeling. It uses an LLM to generate rich column embeddings and then employs a graph-based module to inject inter-column relationships. Experiments demonstrate that TabEmb consistently outperforms existing methods on various table annotation tasks, highlighting the importance of joint semantic-structural representations.
LLMs can be effectively combined with graph-based methods to capture both semantic and structural information in tables, leading to state-of-the-art performance in table annotation tasks.
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 BERT. However, this leads to limited semantic quality and weaker generalization to unseen or rare values compared to modern LLMs, and degraded structural modeling due to 2D-to-1D flattening and context-length constraints. We propose TabEmb, which directly targets these limitations by decoupling semantic encoding from structural modeling. An LLM first produces semantically rich embeddings for each column, and a graph-based module over columns then injects relationships into the embeddings, yielding joint semantic-tructural representations for table annotation. Experiments show that TabEmb consistently outperforms strong baselines on different table annotation tasks. Source code and datasets are available at https://github.com/hoseinzadeehsan/TabEmb
