Machine learning techniques for semantic processing of structured natural language documents to detect action items

A computer-implemented method for generating an action item log user interface element for a webpage that displays content data associated with a document data object, the computer-implemented method comprising:

• parsing the content data to identify a plurality of content segmentation units, wherein the document data object is a structured document data object that is associated with a structural scheme, and wherein the plurality of content segmentation units comprise one or more predefined structural elements of the document data object that are determined based on the structural scheme;
• applying an action item classification machine learning model to each content segmentation unit of the plurality of content segmentation units to determine an action item presence prediction for each content segmentation unit, wherein the action item classification machine learning model comprises a Bidirectional Encoder Representations from Transformers (BERT) model;
• determining, based on each action item presence prediction for each respective content segmentation unit, a candidate action item subset of the plurality of content segmentation units;
• applying an action item extraction machine learning model to the candidate action item subset to generate an action item set;
• and
• generating one or more action item log user interface elements configured for rendering to a computing device display based on the action item set.

A computer program product for generating action item log user interface element for a webpage that displays content data associated with a document data object, the computer program product comprising at least one non-transitory computer-readable storage medium having computer-readable program code portions stored therein, the computer-readable program code portions configured to:

• parse the content data to identify a plurality of content segmentation units, wherein the document data object is a structured document data object that is associated with a structural scheme, and wherein the plurality of content segmentation units comprise one or more predefined structural elements of the document data object that are determined based on the structural scheme;
• apply an action item classification machine learning model to each content segmentation unit of the plurality of content segmentation units to determine an action item presence prediction for each content segmentation unit, wherein the action item classification machine learning model comprises a Bidirectional Encoder Representations from Transformers (BERT) model;
• determine, based on each action item presence prediction for each respective content segmentation unit, a candidate action item subset of the plurality of content segmentation units;
• apply an action item extraction machine learning model to the candidate action item subset to generate an action item set;
• and
• generate one or more action item log user interface elements configured for rendering to a computing device display based on the action item set.