Abstract: Systems and methods for contextual natural language censoring are disclosed. For example, configuration data indicating details associated with content provided by a client device and/or about the client device may be received and may be utilized to determine impermissible and permissible exceptions for a given client. One or more queries may be generated utilizing the impermissible and permissible exceptions, and when input data is received from the client device and/or in association with the client identifier, the queries may be utilized to evaluate the input data for impermissible and permissible exceptions. The results may be filtered based on user preferences, the input data may be censored, the input data may be prevented from being exposed to a user device, the application associated with the input data may be removed from availability, and/or a maturity setting may be changed for the application, for example.

Abstract: Systems and methods for natural language processing policies are disclosed. For example, content such as a response to a user command and/or user input such as a customer review, may be analyzed to determine if the content deviates from one or more content policies and to determine a severity of the content-policy deviation(s). A system may determine a number of content-policy deviations associated with the content and whether the content includes an indicator of a person, such as a pronoun and/or a proper noun. Content that includes multiple content-policy deviations and/or than includes an indicator of a person may be determined to be more severe than other deviated content. Applications associated with deviated content may be ranked and corrective action may be taken based on the ranking.

Abstract: Systems and methods for natural language processing policies are disclosed. For example, content such as a response to a user command and/or user input such as a customer review, may be analyzed to determine if the content deviates from one or more content policies and to determine a severity of the content-policy deviation(s). A system may determine a number of content-policy deviations associated with the content and whether the content includes an indicator of a person, such as a pronoun and/or a proper noun. Content that includes multiple content-policy deviations and/or than includes an indicator of a person may be determined to be more severe than other deviated content. Applications associated with deviated content may be ranked and corrective action may be taken based on the ranking.