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arXiv · 1708.04120

Putting Self-Supervised Token Embedding on the Tables

Marc Szafraniec·Gautier Marti·Philippe Donnat
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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.

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Marc Szafraniec, Gautier Marti, Philippe Donnat. 2017-07-28. Putting Self-Supervised Token Embedding on the Tables. https://arxiv.org/abs/1708.04120

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