Prediction of table column items in unstructured documents using a hybrid model

A non-transitory storage medium having stored therein instructions that are executable by one or more hardware processors to perform operations comprising:

• collecting training data that comprises annotated unstructured documents that each include one or more tables with words whose respective column indices are known;
• training a model, using the training data, to detect a table body and a table header in a given document;
• identifying, by the model, a region of a document that corresponds to a table header of a table in a new document that is not part of the training data;
• using a rule-based algorithm to:
• perform a segmentation process on the table header that returns a list of column boundaries identified in the table header;
• and
• use the identified column boundaries to preliminarily assign a respective column index to each word in the table header;
• and
• running a graph neural network model on a graph that includes nodes based on the words in the table and edges based on the identified column boundaries,
• wherein running the graph neural network generates a refined prediction of a respective column index for each of the words in the table of the new document.