Title: Putting Self-Supervised Token Embedding on the Tables,
Abstract: Information distribution by electronic messages is a privileged means of
transmission for many businesses and individuals, often under the form of
plain-text tables. As their number grows, it becomes necessary to use an
algorithm to extract text and numbers instead of a human. Usual methods are
focused on regular expressions or on a strict structure in the data, but are
not efficient when we have many variations, fuzzy structure or implicit labels.
In this paper we introduce SC2T, a totally self-supervised model for
constructing vector representations of tokens in semi-structured messages by
using characters and context levels that address these issues. It can then be
used for an unsupervised labeling of tokens, or be the basis for a
semi-supervised information extraction system.