1. A computer readable storage medium including executable instructions which, when executed, assist a user by: receiving input associated with a user operation; storing the input in a store component in a chronological order; predicting a number of textual candidates based in part on the user operation and a number of preceding textual words in the store component, wherein the predicting comprises: determining a number of textual candidate probabilities, wherein each textual candidate probability is based in part on a first quantity that corresponds with a number of times the textual candidate follows the number of preceding textual words in the store component and the number of times that the number of preceding textual words occur in the store component and a second quantity that corresponds with a number of times the textual candidate is observed in the store component and the total number of words in the store component; selecting a number of top textual candidates based on the textual candidate probabilities; determining adjusted probabilities of the selected top textual candidates, wherein each adjusted candidate probability is based in part on a candidate rank function multiplied by a first variable and an associated textual candidate probability multiplied by a second variable, wherein the candidate rank function is based in part on a third quantity that corresponds with the selected top textual candidate rank values; and displaying the selected number of top textual candidates ranked by the adjusted candidate probabilities.
1. A computer readable storage medium including executable instructions which, when executed, assist a user by: receiving input associated with a user operation; storing the input in a store component in a chronological order; predicting a number of textual candidates based in part on the user operation and a number of preceding textual words in the store component, wherein the predicting comprises: determining a number of textual candidate probabilities, wherein each textual candidate probability is based in part on a first quantity that corresponds with a number of times the textual candidate follows the number of preceding textual words in the store component and the number of times that the number of preceding textual words occur in the store component and a second quantity that corresponds with a number of times the textual candidate is observed in the store component and the total number of words in the store component; selecting a number of top textual candidates based on the textual candidate probabilities; determining adjusted probabilities of the selected top textual candidates, wherein each adjusted candidate probability is based in part on a candidate rank function multiplied by a first variable and an associated textual candidate probability multiplied by a second variable, wherein the candidate rank function is based in part on a third quantity that corresponds with the selected top textual candidate rank values; and displaying the selected number of top textual candidates ranked by the adjusted candidate probabilities. 10. The computer-readable storage medium of claim 1 , wherein the instructions, when executed, assist a user by predicting at least one of a next word or phrase using a prediction model that includes: P ⁡ ( w n ⁢ | ⁢ w n - N + 1 n - 1 ) = k n ⁢ C ⁡ ( w n - N + 1 n ) C ⁡ ( w n - N + 1 n - 1 ) + k n - 1 ⁢ C ⁡ ( w n - N + 2 n ) C ⁡ ( w n - N + 2 n - 1 ) + … + k 1 ⁢ C ⁡ ( w n ) n wherein, w represents a word where w 1 is the oldest word, w 2 is the next oldest word, . . . and w n is the next word or phrase to be predicted; w m n represents a sequence of words, from w n , to w n ; W n−N+1 . . . w n−1 represents a sequence of preceding candidates; P(w n ) represents the probability of w n ; P(w n |w m n−1 ) represents the probability of the at least one of the next word or phrase (w n ) given a preceding sequence w m n−1 ; C(w m n ) represents a number of times that a sequence of candidates w m . . . w n exists in a data store; k i represents an interpolation coefficient, where Σ i k i =1; n represents a number of words in the data store; and, N represents a number that specifies a length of a candidate sequence used to predict a number of candidates.