Abstract: A natural language understanding (NLU) framework includes an ensemble scoring system that uses received indicators, along with a set of ensemble scoring weights and ensemble scoring rules, to determine a respective ensemble score for each artifact of the utterance identified during inference. The ensemble scoring rules enable boosting of the respective ensemble score of an extracted intent of an utterance in response to a sufficient or important entity associated with the intent also being extracted from the utterance. Based on one or more ensemble scoring rules, the ensemble scoring system may refer to an intent-entity model to determine sufficient or important entities associated with an extracted intent, and boost the respective ensemble artifact score of the intent when the ensemble scoring system determines, with a suitable confidence, that a sufficient entity or important entity of the intent was extracted by the NLU framework during inference of the user utterance.

Abstract: A natural language understanding (NLU) framework includes an ensemble scoring system designed to receive indicators determined by various systems of the NLU framework when inferencing a user utterance. The ensemble scoring system uses the received indicators, along with a set of ensemble scoring weights, to determine a respective ensemble score for each artifact of the utterance identified during inference. For example, segmentations provided by a lookup source system may be used to boost scores of intent and/or entities identified during a meaning search operation of a NLU system. The NLU framework may also include an ensemble scoring weight optimization subsystem that automatically determines optimized ensemble scoring weight values from labeled training data using an optimization plugin. Accordingly, the NLU framework enables these indicators to be suitably weighted and combined to provide a desired level of performance (e.g.

Abstract: A natural language understanding (NLU) framework includes an a concept system that performs concept matching of user utterances. The concept system generates a concept cluster model from sample utterances of an intent-entity model, and then trains a machine learning (ML) concept model based on the concept cluster model. Once trained, the concept model receives semantic vectors representing potential concepts extracted from utterances, and provides concept indicators to an ensemble scoring system. These concept indicators include indications of which concepts of the concept model that matched to the potential concepts, which intents of the intent-entity model are related to these concepts, and concept-relationship scores indicating a strength and/or uniqueness of the relationship between each concept-intent combination.

Abstract: A natural language understanding (NLU) framework includes a lookup source system having one or more lookup sources. Each lookup source includes a respective source data representation that is compiled from respective source data. Once compiled, a user utterance can be submitted to the lookup source system, which generates segmentations of the user utterance. Each segmentation generally includes a collection of non-overlapping segments, and each segment generally describes how tokens of the user utterance can be grouped together and matched to the states of the source data representations. During lookup source inference, matches can be made to produced states or using fuzzy matchers that have corresponding of scoring adjustments. These scoring adjustments may be used by a segmentation scoring subsystem, potentially in combination with one or more additional segmentation scoring plugins, to score and rank the segmentations determined by the lookup source system for the user utterance.