Abstract: Resolving ambiguities in regulatory documents is necessary to ensure organizations and people are able to be best possible compliant with regulations or standards. Current approaches attempting to automatically resolve ambiguities in regulatory documents have limitations when it comes to incorporating fairness or reduce chances of subjective interpretation. Embodiments of the present disclosure provide a method and system for automatically resolving ambiguities in regulations. To disambiguate a given regulatory sentence the method augments the regulation sentence with relevant internal information extracted using a set of predefined linguistic patterns and relevant external information extracted from external sources identified using a Neural Network (NN) model.

Abstract: This disclosure relates generally to data processing, and more particularly to a method and system for generating ground truth labels for ambiguous domain specific tasks. The system generates reference data corresponding to a regulation statement being processed, using a crowd sourcing mechanism and then processes the reference data using an Expectation Maximization (EM) model. The EM model determines consensus with respect to ambiguity of terms/phrases, validity of questions, and validity of answers, and then based on the determined consensus, provides questions and answers to disambiguate the regulation statement.

Abstract: Resolving ambiguities in regulatory documents is necessary to ensure organizations and people are able to be best possible compliant with regulations or standards. Current approaches attempting to automatically resolve ambiguities in regulatory documents have limitations when it comes to incorporating fairness or reduce chances of subjective interpretation. Embodiments of the present disclosure provide a method and system for automatically resolving ambiguities in regulations. To disambiguate a given regulatory sentence the method augments the regulation sentence with relevant internal information extracted using a set of predefined linguistic patterns and relevant external information extracted from external sources identified using a Neural Network (NN) model.

Abstract: This disclosure relates generally to data processing, and more particularly to a method and system for generating ground truth labels for ambiguous domain specific tasks. The system generates reference data corresponding to a regulation statement being processed, using a crowd sourcing mechanism and then processes the reference data using an Expectation Maximization (EM) model. The EM model determines consensus with respect to ambiguity of terms/phrases, validity of questions, and validity of answers, and then based on the determined consensus, provides questions and answers to disambiguate the regulation statement.