Methods for natural language model training in natural language understanding (NLU) systems
Systems and methods for generating a query using a trained 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. In response to determining whether the obsequious expression describes the content entity, the query is generated. In response to determining the obsequious expression describes the content entity, the content entity and the obsequious expression are included in the query and in response to determining the obsequious expression does not describe the content entity, the content entity is included in the query and the obsequious expression is excluded from the query.
1. A method of generating a query using a trained 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;
in response to determining whether the obsequious expression describes the content entity, generating the query by:
in response to determining the obsequious expression describes the content entity, including the content entity and the obsequious expression in the query; and
in response to determining the obsequious expression does not describe the content entity, including the content entity in the query and excluding the obsequious expression from the query.
2. The method of claim 1 , further comprising in response to determining whether text string does not include an obsequious expression, including the content entity in the query.
3. The method of claim 1 , further comprising transmitting the query to the natural language model to train the natural language model with the query.
4. 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.
5. 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.
6. The method of claim 1 , further comprising updating a database with the content entity.
7. The method of claim 1 , further comprising updating a database with the obsequious expression.
8. A system for generating a query using a trained 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;
in response to determining whether the obsequious expression describes the content entity, generate the query by:
in response to determining the obsequious expression describes the content entity, include the content entity and the obsequious expression in the query; and
in response to determining the obsequious expression does not describe the content entity, include the content entity in the query and excluding the obsequious expression from the query.
9. The system of claim 8 , wherein the control circuitry is further configured to in response to determining whether text string does not include an obsequious expression, include the content entity in the query.
10. The system of claim 8 , 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.
11. The system of claim 8 , wherein determining whether the text string includes an obsequious expression, the control circuitry is further configured to compare the obsequious expression to a list of stored obsequious expressions for a match.
12. The system of claim 8 , wherein determining whether the obsequious expression describes the content entity, the control circuitry is further configured to perform a natural language recognition process selected from a group of hidden Markov model, dynamic time warping, and artificial neural networks.
13. The system of claim 8 , the control circuitry is further configured to update a database with the content entity.
14. The system of claim 8 , the control circuitry is further configured to update a database with the obsequious expression.