2. The method of claim 1 , wherein associating the likelihood value with the new character string comprises storing the new character string in at least one n-gram of the language model with the likelihood value, such that the likelihood value corresponds to the at least one n-gram in the language model, the method further comprising: responsive to determining that the third input deletes the new character string: determining a plurality of n-grams in the language model that include the new character string; and decreasing a respective likelihood value that corresponds to each respective n-gram of the plurality of n-grams.
2. The method of claim 1 , wherein associating the likelihood value with the new character string comprises storing the new character string in at least one n-gram of the language model with the likelihood value, such that the likelihood value corresponds to the at least one n-gram in the language model, the method further comprising: responsive to determining that the third input deletes the new character string: determining a plurality of n-grams in the language model that include the new character string; and decreasing a respective likelihood value that corresponds to each respective n-gram of the plurality of n-grams. 3. The method of claim 2 , further comprising: responsive to determining that a first likelihood value that corresponds a higher-order n-gram of the language model is greater than a second likelihood value that corresponds to a lower-order n-gram, modifying the language model to at least: remove the higher-order n-gram, or update the first likelihood value to be less than or equal to the second likelihood value, wherein the higher-order n-gram includes more character strings than the lower-order n-gram.