Identification of domain information for use in machine learning models

A method, comprising:

• receiving, by a device, historical data of an organization associated with a particular domain,
• the historical data including a set of unstructured documents and a set of semi-structured documents;
• analyzing, by the device, the set of unstructured documents using one or more natural language processing techniques to identify a first set of entities;
• determining, by the device, a second set of entities by analyzing the set of semi-structured documents using the one or more natural language processing techniques;
• filtering, by the device, the first set of entities using the second set of entities,
• where filtering the first set of entities includes removing, from the first set of entities, one or more entities that do not satisfy a threshold level of similarity with entities included in the second set of entities;
• consolidating, by the device, the filtered first set of entities and the second set of entities to identify a set of key entities;
• generating, by the device, a data structure that is used to store the set of key entities;
• and
• providing, by the device, the set of key entities to a user device to allow the set of key entities to be annotated and used for one or more machine learning models.

A non-transitory computer-readable medium storing instructions, the instructions comprising:

• one or more instructions that, when executed by one or more processors, cause the one or more processors to:
• receive historical data of an organization associated with a particular domain,
• the historical data including a set of unstructured documents and a set of semi-structured documents;
• determine candidate domain information by analyzing the set of unstructured documents using one or more natural language processing techniques,
• the candidate domain information including at least one of:
• a first set of entities, or
• a first set of values;
• determine additional candidate domain information by analyzing the set of semi-structured documents using the one or more natural language processing techniques,
• the additional candidate domain information including at least one of:
• a second set of entities, or
• a second set of values;
• filter the candidate domain information using the additional candidate domain information;
• filter the additional candidate domain information using the candidate domain information;
• consolidate the filtered candidate domain information and the filtered additional candidate domain information into key domain information;
• generate a data structure that associates values included in the key domain information;
• and
• provide the key domain information to a user device to allow the key domain information to be annotated and used for one or more machine learning models.