Methods for natural language model training in natural language understanding (NLU) systems
Systems and methods for training a natural language model of a natural language understanding (NLU) system are disclosed herein. A text string including at least a content entity is received. A determination is made as to whether the text string includes an obsequious expression. In response to determining the text string includes an obsequious expression, a determination is made as to whether the obsequious expression describes the content entity. A query is forwarded in response to determining the text string includes an obsequious expression and in determining the obsequious expression describes the content entity. In response to determining the obsequious expression describes the content entity, the query includes the obsequious expression and in response to determining the obsequious expression does not describe the content entity, the query does not include the obsequious expression.
1. A method of training a natural language model of a natural language understanding (NLU) system, the method comprising:
receiving a text string including at least a content entity;
determining whether the text string includes an obsequious expression;
in response to determining the text string includes an obsequious expression, determining whether the obsequious expression describes the content entity;
forwarding a query with the content entity to the natural language model,
wherein:
in response to determining the obsequious expression describes the content entity, the query includes the obsequious expression; and
in response to determining the obsequious expression does not describe the content entity, the query does not include the obsequious expression.
2. The method of claim 1 , further comprising transmitting the query to the natural language model to train the natural language model with the query.
3. The method of claim 1 , wherein determining whether the text string includes an obsequious expression comprises comparing the obsequious expression to a list of stored obsequious expressions for a match.
4. The method of claim 1 , wherein determining whether the obsequious expression describes the content entity comprises performing a natural language recognition process selected from a group of hidden Markov model, dynamic time warping, and artificial neural networks.
5. The method of claim 1 , further comprising updating a database with the content entity.
6. The method of claim 1 , further comprising updating a database with the obsequious expression.
7. A system for training a natural language model of a natural language understanding (NLU) system, the system comprising:
input circuitry configured to receive a text string including at least a content entity;
control circuitry configured to:
determine whether the text string includes an obsequious expression;
in response to determining the text string includes an obsequious expression, determine whether the obsequious expression describes the content entity;
forward a query with the content entity to the natural language model, wherein:
in response to determining the obsequious expression describes the content entity, the query includes the obsequious expression; and
in response to determining the obsequious expression does not describe the content entity, the query does not include the obsequious expression.
8. The system of claim 7 , wherein the control circuitry is further configured to transmit the query to the natural language model to train the natural language model with the query.
9. The system of claim 7 , wherein the control circuitry is further configured to determine whether the text string includes an obsequious expression comprises comparing the obsequious expression to a list of stored obsequious expressions for a match.
10. The system of claim 7 , wherein the to determine whether the obsequious expression describes the content entity, the control circuitry is further configured a natural language recognition process selected from a group of hidden Markov model, dynamic time warping, and artificial neural networks.
11. The system of claim 7 , wherein the control circuitry is further configured to update a database with the content entity.
12. The system of claim 7 , wherein the control circuitry is further configured to update a database with the obsequious expression.