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, systems, methods, and computer-readable media for selective feature activation for coreference resolution are disclosed. In one embodiment, a method includes receiving text data comprising a plurality of mentions corresponding to entities, and determining a plurality of data features, comprising semantic features and syntactic features, for comparing a particular pair of mentions from the plurality of mentions. The method also includes selectively activating a subset of features from the plurality of data features based on semantic and syntactic context of the particular pair of mentions within the text data, and determining, using weights associated with the activated subset of features and at least one machine learning function, whether a first mention of the pair of mentions and second mention of the pair of mentions refer to a same entity.

Abstract: Systems and methods for coreference resolution are disclosed. In one embodiment, a method includes locating, for each of a selected plurality of chains of coreferent mentions, a particular context-based name from the respective chain, wherein the coreferent mentions correspond to entities and the context-based name is a longest name in the respective chain, a last name in the respective chain, or a most frequently occurring name in the respective chain. The method also includes determining an entity category for each respective one of the plurality of chains and determining one or more entity attributes from structured data and unstructured data. The method further includes, based on the located particular context-based name, the entity category, and the one or more attributes, assigning high-probability coreferent chains to high-confidence buckets, such as to produce a Zipfian-like distribution having a head region and a tail region.

Abstract: According to one aspect, a method for coreference resolution is provided. In one embodiment, the method includes receiving a segment of text that includes mentions corresponding to entities. A first feature vector is generated based on one or more features associated with a first mention, and a second feature vector is generated based on based on one or more features associated with a second mention. A measure of similarity between the first feature vector and second feature vector is computed and, based on the computed measure of similarity, it is determined if the first mention and the second mention both correspond to the same entity.