The disclosure relates to systems and methods for identifying entities related to a task in a natural language input. An entity detection model is provided which receives a natural language input. The entity detection model processes the natural language input using an entity encoder and an input encoder. The entity encoder identifies and encodes relevant entities while the input encoder generates a contextual encoding which represents contextual information associated with a relevant entity. The encoded entity and contextual encodings may then be combined and processed to generate a probability score for an identified entity. A negation constraint model is also disclosed. The negation constraint model receives the natural language input and the identified entities. The natural language input is analyzed to identify negation cues and determine if the negation cue is associated with an identified entity.
SYSTEMS AND METHODS FOR IDENTIFYING ENTITIES AND CONSTRAINTS IN NATURAL LANGUAGE INPUT