  Tables contain valuable knowledge in a structured form. We employ neural
language modeling approaches to embed tabular data into vector spaces.
Specifically, we consider different table elements, such caption, column
headings, and cells, for training word and entity embeddings. These embeddings
are then utilized in three particular table-related tasks, row population,
column population, and table retrieval, by incorporating them into existing
retrieval models as additional semantic similarity signals. Evaluation results
show that table embeddings can significantly improve upon the performance of
state-of-the-art baselines.
