27. The method of claim 23 wherein the word n-grams include word unigrams, word bigrams and word trigrams.
23. A computer-implementable method of training a machine-learned classifier, the method comprising: generating, by using computer readable instructions executable by a processor, a plurality of training-time feature vectors by automatically extracting features, which include grammar and spelling features, word n-grams and linguistic analysis features based on automatic syntactic and semantic analysis, from first and last versions of training documents, and combining the extracted grammar and spelling features, the extracted word n-grams and the extracted linguistic analysis features to form the plurality of training-time feature vectors, wherein the first versions of the training documents are unedited documents that represent a first class of text and wherein the last versions of the training documents are edited documents that represent a second class of text; and training, with the help of the processor, the machine-learned classifier based on the plurality of training-time feature vectors, the machine-learned classifier being capable of providing an assessment of an editorial quality of a textual unit based on a degree of similarity in quality of the textual unit to either the first versions of the training documents that represent the first class of text or the last versions of the training documents that represent the second class of text, and wherein the linguistic analysis features include at least one logical form feature, and wherein each of the plurality of training-time feature vectors includes a designator of the editorial quality of a training document, of the training documents, to which it corresponds.