Extraction machine learning framework

A system comprising:

• at least one processor;
• an application, when executed by the at least one processor, performs:
• extracting a set of features from a table;
• applying a feature extraction model to the set of features to generate a pseudo sentence;
• applying a classifier model to the pseudo sentence to generate a label prediction for the table, wherein the classifier model is trained with an augmented training pseudo sentence created by one or more of sampling and shuffling;
• and
• updating a label of the table with the label prediction.

A non-transitory computer readable medium comprising instructions that, when executed by at least one processor, cause the at least one processor to perform:

• extracting a set of features from a table;
• applying a feature extraction model to the set of features to generate a pseudo sentence;
• applying a classifier model to the pseudo sentence to generate a label prediction for the table, wherein the classifier model is trained with an augmented training pseudo sentence created by one or more of sampling and shuffling;
• and
• updating a label of the table with the label prediction.