Abstract: Training and using a machine learning model for data extraction is provided. The method comprises receiving keys of interest received from a user through an interface and receiving a batch of documents containing unstructured text. Unstructured text of a first document is processed to extract structured text. The model predicts text classifications of the structured text according to the keys of interest. The predicted text classifications are output to the user through the interface. Annotations to correct any incorrect predictions are received from the user, and the model is retrained according to the annotations. The above steps are repeated for less than ten additional documents from the batch until the model has been trained to predict text classifications with a specified level of accuracy. The trained model then classifies extracted structured text in the remaining documents in the batch.

Abstract: A method of information retrieval is provided. The method comprises receiving a query from a user and parsing the query in real-time as the user enters the query. The parsed query is interpreted dynamically based on a defined schema of a knowledge base, and a number of query interpretations is displayed in real-time as the user enters the query. When a selection of one of the query interpretations is received from the user information is retrieved from the knowledge base according to the selected query interpretation.

Abstract: A method of information retrieval is provided. The method comprises receiving a query from a user and parsing the query in real-time as the user enters the query. The parsed query is interpreted dynamically based on a defined schema of a knowledge base, and a number of query interpretations is displayed in real-time as the user enters the query. When a selection of one of the query interpretations is received from the user information is retrieved from the knowledge base according to the selected query interpretation.