Document structure extraction using machine learning

A document structure extraction method comprising:

• receiving, by a document structure analytics server, an untagged document that comprises a plurality of document parts, wherein certain of the document parts have a visual appearance that is defined by formatting information included in the untagged document;
• receiving, by the document structure analytics server, a command to generate a table of contents for the untagged document;
• in response to receiving the command to generate the table of contents, invoking a document tagging process that comprises:
• identifying a document type category to which the untagged document belongs;
• extracting at least a portion of the formatting information from the untagged document;
• for each of two or more of the plurality of document parts, generating one or more feature-value pairs using the extracted formatting information, wherein each of the generated feature-value pairs characterizes the visual appearance of the corresponding document part by associating a particular value with a particular formatting feature;
• making a selection of a particular predictive model, from amongst a plurality of predictive models hosted by the document structure analytics server, wherein the selection is made based on the particular predictive model having been trained using a corpus of tagged training documents belonging to the identified document type category to which the untagged document belongs, and wherein each of the predictive models is configured to categorize document parts for documents sharing a common document type categorization for a respective predictive model;
• using the particular predictive model to predict a categorization for each of the two or more document parts that form part of the untagged document based on the corresponding one or more feature-value pairs, wherein the particular predictive model applies a machine learning algorithm to make predictions based on a collection of categorized feature-value pairs aggregated from, and characterizing document parts included in, the corpus of tagged training documents belonging to the identified document type category;
• and
• defining tag metadata that associates each of the two or more document parts with the corresponding predicted categorization generated by the particular predictive model;
• generating the table of contents based on the defined tag metadata, wherein the table of contents correlates a document part identified as a heading by the particular predictive model with a location of the heading within the untagged document;
• and
• modifying the untagged document to include the generated table of contents.