Abstract: Disclosed herein is a system and method for machine learning extraction of free-form textual rules and provisions from legal documents. The method comprising electronically receiving, by the legal rules extraction engine, a document, processing the document using a first trained model executed by the legal rules extraction engine to classify the document into a document class, processing the document using a second trained model executed by the legal rules extraction engine to extract rules within the document conditional on the document class identified by the first trained model, extracting a plurality of data variables from the document by processing the classified features in the document using a third trained model executed by the legal rules extraction engine, generating by the legal rules extraction engine an output vector based on the plurality of data variables, and displaying the output vector by the legal rules extraction engine at the user interface.

Abstract: Systems and methods for extracting table data from text documents using machine learning are provided. The systems and methods comprise electronically receiving at a computer system a document having one or more tables, each table having one or more whitespace features, processing the document using a first computer model executed by the computer system to classify each row of the one or more tables as a header row or a data row, processing the document using a second computer model executed by the computer system to classify each whitespace feature in each row conditional on classification of each row by the first computer model, the second computer model identifying whether a whitespace feature corresponds to information missing from the one or more tables, and generating an output of the classified whitespace features and storing the output in a digital file.