10. A computer-implemented method, comprising: partitioning, by the one or more computers, a collection of web documents into a training corpus and a development corpus; training, by the one or more computers, a language model on the training corpus for first word probabilities of words in the training corpus, wherein the words in the training corpus include a candidate word defined by a sequence of two or more characters, the two or more characters defining two or more corresponding words in the training corpus, the two or more corresponding words existing words in a dictionary; counting, by the one or more computers, occurrences of the candidate word and the two or more corresponding words in the development corpus; determining, by the one or more computers, a first value based on the occurrences of the candidate word in the development corpus and the first word probabilities; determining, by the one or more computers, a second valued based on the occurrences of the two or more corresponding words in the development corpus and the first word probabilities; and comparing, by the one or more computers, the first value to the second value; and determining, by the one or more computers, whether the candidate word is a new word based on the comparison.
10. A computer-implemented method, comprising: partitioning, by the one or more computers, a collection of web documents into a training corpus and a development corpus; training, by the one or more computers, a language model on the training corpus for first word probabilities of words in the training corpus, wherein the words in the training corpus include a candidate word defined by a sequence of two or more characters, the two or more characters defining two or more corresponding words in the training corpus, the two or more corresponding words existing words in a dictionary; counting, by the one or more computers, occurrences of the candidate word and the two or more corresponding words in the development corpus; determining, by the one or more computers, a first value based on the occurrences of the candidate word in the development corpus and the first word probabilities; determining, by the one or more computers, a second valued based on the occurrences of the two or more corresponding words in the development corpus and the first word probabilities; and comparing, by the one or more computers, the first value to the second value; and determining, by the one or more computers, whether the candidate word is a new word based on the comparison. 11. The method of claim 10 , wherein training a language model on the training corpus for first word probabilities of words in the training corpus comprises training an n-gram language model.