1. A computer-implemented method, comprising: identifying a word corpus; determining a weight based on a type of the word corpus, the weight indicating a degree of reliability for updating an existing input method editor dictionary using the word corpus; associating a word probability value with each word in the word corpus; identifying a sentence; determining candidate segmentations of the sentence based on the word corpus; determining a segmentation probability value for each of the candidate segmentations, each candidate segmentation having one or more segments that define words forming the sentence; determining a weighted segmentation probability value for each segmentation probability value based on the weight; determining, in a data processing apparatus, a soft-count value for each word in the word corpus based on the weighted segmentation probability values of the candidate segmentations in which the word appears and a number of occurrences of the word in the candidate segmentations in which the word appears; iteratively adjusting the associated word probability value for each word in the word corpus based on the soft-count value for the word; and updating the existing input method editor dictionary by iteratively adding a plurality of words from the word corpus and their respective associated word probability values to the existing input method editor dictionary, wherein the plurality of words from the word corpus are words having the highest associated word probability values.
1. A computer-implemented method, comprising: identifying a word corpus; determining a weight based on a type of the word corpus, the weight indicating a degree of reliability for updating an existing input method editor dictionary using the word corpus; associating a word probability value with each word in the word corpus; identifying a sentence; determining candidate segmentations of the sentence based on the word corpus; determining a segmentation probability value for each of the candidate segmentations, each candidate segmentation having one or more segments that define words forming the sentence; determining a weighted segmentation probability value for each segmentation probability value based on the weight; determining, in a data processing apparatus, a soft-count value for each word in the word corpus based on the weighted segmentation probability values of the candidate segmentations in which the word appears and a number of occurrences of the word in the candidate segmentations in which the word appears; iteratively adjusting the associated word probability value for each word in the word corpus based on the soft-count value for the word; and updating the existing input method editor dictionary by iteratively adding a plurality of words from the word corpus and their respective associated word probability values to the existing input method editor dictionary, wherein the plurality of words from the word corpus are words having the highest associated word probability values. 7. The method of claim 1 wherein determining a soft-count value of a word in the sentence comprises multiplying a sum of the weighted segmentation probability values of candidate segmentations of a substring before the word, the probability value of the word, and a sum of the weighted segmentation probability values of candidate segmentations of a substring after the word.