Abstract: A system and method for creating an electronic document chronology. The method includes applying a machine learning model to an application data set to determine a plurality of connecting events representing a plurality of electronic document changes, wherein each connecting event is between a first entity and a second entity of a plurality of entities, wherein the first entity of each connecting event is an electronic document, wherein the application data set includes first electronic document change data and a plurality of first entity identifiers of the plurality of entities; and creating a document chronology graph based on the plurality of connecting events, wherein the document chronology graph includes a plurality of nodes and a plurality of edges, wherein each node represents one of the plurality of entities, wherein each edge represents one of the plurality of connecting events.

Abstract: A system and method for creating an electronic document chronology. The method includes applying a machine learning model to an application data set to determine a plurality of connecting events representing a plurality of electronic document changes, wherein each connecting event is between a first entity and a second entity of a plurality of entities, wherein the first entity of each connecting event is an electronic document, wherein the application data set includes first electronic document change data and a plurality of first entity identifiers of the plurality of entities; and creating a document chronology graph based on the plurality of connecting events, wherein the document chronology graph includes a plurality of nodes and a plurality of edges, wherein each node represents one of the plurality of entities, wherein each edge represents one of the plurality of connecting events.

Abstract: Natural language learning in context is provided by generating combined text of a user's native tongue and language to be learned. The combined text is generated based on elements of code-switching including syntax and semantics. Combining text based on elements of code-switching maximizes the learnability or the likelihood of retaining certain text of a foreign language.

Abstract: Competency of a participant is based on the probability of a participant selecting a particular answer is a function of that participant's ability (or ranking) and the correctness of the answer (either presented to or created by the participant). The participant's competency—or level of understanding of the content—is used to generate optimal test content.

Abstract: Natural language learning in context is provided by generating combined text of a user's native tongue and language to be learned. The combined text is generated based on elements of code-switching including syntax and semantics. Combining text based on elements of code-switching maximizes the learnability or the likelihood of retaining certain text of a foreign language.