Abstract: Described herein is a regulatory parser that downloads and efficiently processes regulatory documents. The regulatory documents may be from different sources and may have different formats. The regulatory parser parses all of the text in the regulatory documents and converts into a predetermined, single format for downstream applications. The text is organized and stored in a structured tree, organized into one or more hierarchies with nodes storing segments of text from a regulatory document. In some embodiments, each node in the regulatory tree may represent a segment of text. Partitioning the text of a regulatory document into segments of text may make the storage and querying of the regulatory documents more manageable. The organization and structure of the structured tree may reduce the times and resources needed for accessing and searching for a regulatory citation. The structured tree may allow a user to manipulate a regulatory document or text.

Abstract: Described herein is a machine-learning model that categorizes and classifies regulatory text and methods for operation thereof. The machine-learning model may receive raw data. The raw data may be data in a file that includes a list of text examples (e.g., leaf node citation texts). One or more datasets may be annotated. A training, validation, and test dataset may be generated. The machine-learning model is used to determine one or more predictions regarding the category and classification of input data. The training dataset is used to train the machine-learning model, the validation dataset is used to tune the hyper parameters of the model, and the test dataset is used to evaluate its performance. The prediction(s) are stored or sent to one or more downstream applications.