Abstract: Some aspects of the present disclosure relate to systems and methods for identifying potential violation conditions from electronic communications. In one embodiment, a method includes receiving data associated with an electronic communication and detecting, from the received data, and using a trainable model, an indicator of a potential violation condition, where the violation condition is associated with an activity that is a violation of a predetermined standard. The method also includes, responsive to detecting the indicator of the potential violation condition, marking the electronic communication as being associated with a potential violation condition, and presenting the potential violation condition to a user for review.

Abstract: Some aspects of the present disclosure relate to systems and methods for identifying potential violation conditions from electronic communications. In one embodiment, a method includes receiving data associated with an electronic communication and detecting, from the received data, and using a trainable model, an indicator of a potential violation condition, where the violation condition is associated with an activity that is a violation of a predetermined standard. The method also includes, responsive to detecting the indicator of the potential violation condition, marking the electronic communication as being associated with a potential violation condition, and presenting the potential violation condition to a user for review.

Abstract: In some aspects, the present disclosure relates to coreference resolution. In one embodiment, a method includes obtaining unstructured text data including a plurality of references corresponding to entities, and determining, from the unstructured text data, attributes associated with the entities. The method also includes obtaining structured data including predefined attributes associated with the entities, and comparing attributes associated with a first coreference unit with attributes associated with a second coreference unit. The first coreference unit is a sub-entity representation having the attributes determined from the unstructured text data and the second coreference unit is a sub-entity representation having the predefined attributes. The method further includes determining, based on the comparison, whether the first coreference unit and the second coreference unit both correspond to the same entity.

Abstract: According to one aspect, a method for machine learning management is provided. In one embodiment, the method includes receiving a first segment of text data, identifying data features corresponding to a sequence of characters in the first segment of text data, and generating predictive annotations to the sequence of characters based at least in part on the identified data features. The method can also include identifying inaccurate annotations generated according to the predictive annotations, correcting the identified inaccurate annotations, generating one or more sets of model training data incorporating the corrected annotations, and monitoring progress of annotations made to a second segment of text data associated with the first segment of text data by a plurality of collaborating users of a plurality of managed computers.