Abstract: A system and method for inconsistency detection. A method includes semantically analyzing a first set of data to extract features. The features include subjects represented in the first set of data. Semantically analyzing the first set of data includes applying a machine learning model. The first set of data is consolidated into a knowledge base based on the extracted features. The knowledge base includes a graph having nodes and edges. The nodes represent the subjects, and the edges represent connections among the subjects. The knowledge base is queried based on a second set of data in order to obtain knowledge base query results. Querying the knowledge base includes semantically analyzing the second set of data in order to identify more subjects. Semantically analyzing the second set of data includes applying the machine learning model. Data among the second set of data is validated based on the knowledge base query results.

Abstract: A method and system for training machine learning models using natural language interactions as well as techniques utilizing machine learning models trained using natural language interactions. A method includes applying a language model to text of a set of natural language interactions in order to output a set of domain-specific language (DSL) data, wherein the set of natural language interactions is between a user and at least one other entity, wherein the set of natural language interactions indicates at least one user-defined concept; querying a knowledge base based on the set of DSL data in order to obtain at least one DSL query result; integrating the at least one DSL query result with a structured representation of the natural language interactions in order to create at least one contextualized DSL query result; and training the language model using the at least one contextualized DSL query result.

Abstract: A system and method for building predictive machine learning models. A method includes: parsing text of a document review protocol in order to extract at least one description of at least one concept to be tagged; tagging at least a portion of a plurality of documents in order to create a plurality of tagged documents by applying a language model to the plurality of documents, wherein tagging the at least a portion of the plurality of documents further comprises querying the language model using at least one query generated based on the extracted at least one description; constructing at least one classifier machine learning model based on the extracted at least one description; and training the at least one classifier machine learning model using a training set, wherein the training set includes the plurality of tagged documents.

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.