1. A computer-implementable method for assessing an editorial quality of a textual unit, 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; training, with the help of the processor, a machine-learned classifier based on the plurality of training-time feature vectors, the machine-learned classifier being capable of classifying the textual unit based on the first class of text and the second class of text; generating, with the help of the processor, a run-time feature vector for the textual unit to be assessed by automatically extracting features from the textual unit; and evaluating, with the help of the processor, the run-time feature vector using the machine-learned classifier to provide, as an output, an assessment of the editorial quality of the textual unit, wherein the assessment of the editorial quality of the textual unit reflects a degree of similarity in quality of the textual unit to either the unedited versions of the training documents that represent the first class of text or the edited 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.
1. A computer-implementable method for assessing an editorial quality of a textual unit, 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; training, with the help of the processor, a machine-learned classifier based on the plurality of training-time feature vectors, the machine-learned classifier being capable of classifying the textual unit based on the first class of text and the second class of text; generating, with the help of the processor, a run-time feature vector for the textual unit to be assessed by automatically extracting features from the textual unit; and evaluating, with the help of the processor, the run-time feature vector using the machine-learned classifier to provide, as an output, an assessment of the editorial quality of the textual unit, wherein the assessment of the editorial quality of the textual unit reflects a degree of similarity in quality of the textual unit to either the unedited versions of the training documents that represent the first class of text or the edited 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. 9. The method of claim 1 wherein the word n-grams include word unigrams, word bigrams and word trigrams.