Hybrid natural language understanding
AssigneeVerint Americas Inc.
InventorsTimothy Seegan, Ian Roy Beaver
Life of this patent
Estimating the term…
Abstract
Hybrid natural language understanding (NLU) systems and methods are provided that capitalize on the strengths of the rule-based models and the statistical models, lowering the cost of development and increasing the speed of construction, without sacrificing control and accuracy. Two models are used for intent recognition, one statistical and one rule-based. Both models define the same set of intents, but the rule-based model is devoid of any grammars or patterns initially. Each model may or may not be hierarchical in that it may be composed of a set of specialized models that are in a tree form or it may be just a singular model.
Claims20
A hybrid natural language understanding (NLU) system, comprising:
- a chatbot coupled to a processor, the chatbot comprising:
- a rule-based NLU module configured to determine a first intent using an input during a conversation between a client device and the chatbot;
- a statistical NLU module configured to determine a second intent using the input;
- and
- a decider configured to determine a third intent using the first intent and the second intent, wherein the third intent is further determined by the decider:
- (1) determining whether both the first intent and the second intent are correct,
- (2) determining whether only the first intent is correct,
- (3) determining whether only the second intent is correct, and
- (4) determining whether neither the first intent nor the second intent is correct, wherein the chatbot is configured to generate and provide a processed language output using the third intent.
A method of hybrid natural language understanding (NLU), the method comprising:
- receiving an input data at a rule-based NLU module comprised within a chatbot, wherein the input data is received during a conversation between a client device and the chatbot;
- receiving the input data at a statistical NLU module comprised within the chatbot;
- determining a first intent at the rule-based NLU module using the input data;
- determining a second intent at the statistical NLU module using the input data;
- determining a third intent at a decider using the first intent and the second intent, and:
- (1) determining whether both the first intent and the second intent are correct,
- (2) determining whether only the first intent is correct,
- (3) determining whether only the second intent is correct, and
- (4) determining whether neither the first intent nor the second intent is correct;
- and
- generating and providing a processed language output from the chatbot using the third intent.
A chatbot system comprising:
- a processor;
- and
- a natural language understanding (NLU) component configured to determine a rule-based NLU intent using an input data from a conversation between a client device and a chatbot, determine a statistical NLU intent using the input data, determine an intent using the rule-based NLU intent and the statistical NLU intent, and by:
- (1) determining whether both the rule-based NLU intent and the statistical NLU intent are correct, (2) determining whether only the rule-based NLU intent is correct, (3) determining whether only the statistical NLU intent is correct, and (4) determining whether neither the rule-based NLU intent nor the statistical NLU intent is correct, and generate and output a processed language output using the intent.
Dependencies are inferred from the claim text.