  There are hundreds of millions of tables in Web pages that contain useful
information for many applications. Leveraging data within these tables is
difficult because of the wide variety of structures, formats and data encoded
in these tables. TabVec is an unsupervised method to embed tables into a vector
space to support classification of tables into categories (entity, relational,
matrix, list, and non-data) with minimal user intervention. TabVec deploys
syntax and semantics of table cells, and embeds the structure of tables in a
table vector space. This enables superior classification of tables even in the
absence of domain annotations. Our evaluations in four real world domains show
that TabVec improves classification accuracy by more than 20% compared to three
state of the art systems, and that those systems require significant in domain
training to achieve good results.
