Abstract: To perform automated database updates, a text document is processed by a natural language processing engine, by way of tagging, by a pre-trained general named entity recognition annotation model and/or at least one domain-specific named entity recognition component, tokens in the text document with named entity recognition annotations. A neural network model processing the tokens and the named entity recognition annotations extracts a database update action from the text document and stores the database update action as structured data in a machine-readable form, which is then sent to a robotic process automation interface and executed by updating a database. This provides an extensible neural architecture for extracting and executing operational actions from text documents. Combining the use of annotated data with a neural network model is more flexible because it allows variations in the exact formulation which is used in the text document to express the database update action.