5. The method according to claim 1 , wherein the method for scoring the candidate word segmentation units is SVM (Support Vector Machine)-based, using an SVM classifier or an SVM regression model to score each candidate word segmentation unit; and representing the candidate word segmentation units, which are data points, as feature vectors so as to train the SVM classifier and the SVM regression model.
5. The method according to claim 1 , wherein the method for scoring the candidate word segmentation units is SVM (Support Vector Machine)-based, using an SVM classifier or an SVM regression model to score each candidate word segmentation unit; and representing the candidate word segmentation units, which are data points, as feature vectors so as to train the SVM classifier and the SVM regression model. 6. The method according to claim 5 , wherein a feature extracted for each candidate word segmentation unit comprises one or a combination of the following features: a number of characters in the candidate word segmentation unit; an average occurrence rate, which is a number of times that the candidate word segmentation unit appears, divided by a number of documents in the search results returned by the search engine; and a document frequency, which is a number of search results containing the candidate word segmentation unit.