13. The system of claim 12 , wherein the hardware processor is further configured to: determine a first plurality of appearance frequencies corresponding to the first plurality of distinct training n-grams; and determine a second plurality of appearance frequencies corresponding to the second plurality of distinct training n-grams; and determining a first anomaly detection score based on the first plurality of appearance frequencies and a second anomaly detection score based on the second plurality of appearance frequencies.
13. The system of claim 12 , wherein the hardware processor is further configured to: determine a first plurality of appearance frequencies corresponding to the first plurality of distinct training n-grams; and determine a second plurality of appearance frequencies corresponding to the second plurality of distinct training n-grams; and determining a first anomaly detection score based on the first plurality of appearance frequencies and a second anomaly detection score based on the second plurality of appearance frequencies. 14. The method of claim 13 , wherein the hardware processor is further configured to: determine which of the first anomaly detection score and the second anomaly detection score is higher; and output the input dataset based on the anomaly detection score that is higher.