You can solve this with a trie of words with letters at each node and a flag indicating whether a particular node marks the end of a word in the trie. When the number of suggestions is too large, if word frequency data is available for the suggestions, limit the suggestions to the top n suggestions based on word frequency. If markov chains of words are available, these could be used instead of raw word frequency (which is really a markov chain of length 1.)

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Interview Question for Software Development Engineer at Microsoft:

Write a program to do the SMS text recognition from the number pad. Basically, the three characters per number to possible text recognition (T9). Did I scare you enough?? :P

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