Methods for natural language model training in natural language understanding (NLU) systems
Systems and methods for training a classifier binary model of a natural language understanding (NLU) system are disclosed herein. A determination is made as to whether a text string, with a content entity, 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. The model is trained based on a determination of at least one of: an absence of an obsequious expression in response to determining the obsequious expression describes the content entity; a presence of an obsequious expression in response to determining the obsequious expression describes the content entity; an absence of an obsequious expression in response to determining the obsequious expression does not describe the content entity, and a presence of an obsequious expression in response to determining the obsequious expression does not describe the content entity.
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 that the text string, which includes the content entity, includes an obsequious expression;
in response to determining the text string includes the obsequious expression, determining whether the obsequious expression describes the content entity; and
training the classifier binary model based on a determination of at least one of:
an absence of an obsequious expression in response to determining the obsequious expression describes the content entity;
a presence of an obsequious expression in response to determining the obsequious expression describes the content entity;
an absence of an obsequious expression in response to determining the obsequious expression does not describe the content entity; and
a presence of an obsequious expression in response to determining the obsequious expression does not describe the content entity.
2. 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.
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 , further comprising in response to determining the text string includes an obsequious expression, updating a database with an indication of presence of an obsequious expression.
5. The method of claim 1 , further comprising in response to determining the text string includes an obsequious expression and in response to determining the obsequious expression does not describe the content entity, updating a database with an indication of presence of the obsequious expression.
6. The method of claim 1 , further comprising in response to determining the text string includes an obsequious expression and in response to determining the obsequious expression describes the content entity, updating a database with an indication of absence of an obsequious expression.
7. A system for training a natural language model of a natural language understanding (NLU) system, the system comprising:
input circuit configured to receive a text string including at least a content entity; and
control circuitry configured to:
receive a text string including at least a content entity;
determine that the text string, which includes the content entity, includes an obsequious expression,
in response to determining the text string includes the obsequious expression, determine whether the obsequious expression describes the content entity; and
train the classifier binary model based on a determination of at least one of:
an absence of an obsequious expression in response to determining the obsequious expression describes the content entity;
a presence of an obsequious expression in response to determining the obsequious expression describes the content entity;
an absence of an obsequious expression in response to determining the obsequious expression does not describe the content entity, and
a presence of an obsequious expression in response to determining the obsequious expression does not describe the content entity.
8. The system of claim 7 , wherein to determine 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.
9. The system of claim 7 , wherein to determine 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.
10. The system of claim 7 , wherein in response to determining the text string includes an obsequious expression, the control circuitry is further configured to update a database with an indication of presence of an obsequious expression.
11. The system of claim 7 , wherein in response to determining the text string includes an obsequious expression and in response to determining the obsequious expression does not describe the content entity, the control circuitry is further configured to update a database with an indication of presence of an obsequious expression.
12. The system of claim 7 , wherein in response to determining the text string includes an obsequious expression and in response to determining the obsequious expression describes the content entity, the control circuitry is further configured to update a database with an indication of absence of an obsequious expression.