1. A method for using human perception in training a language understanding model, comprising: accessing tagged items that are used by the language understanding model; creating queries using the tagged items including errors into a portion of the queries that create a query that affects a weight at least one of the tagged items used within the query being created; obtaining results from a knowledge source for each of the queries created; obtaining a human perception rating for each of the different results; determining a relative importance of each of the tagged items using the human perception ratings; and training the language understanding model using the relative importance of each of the tagged items as determined from the human perception rating.