1. A computer-implemented method of automatically evaluating the linguistic quality of free-response text answers submitted by students in response to examination prompts, the method comprising: (A) configuring a computer device to embody an automated computerized text assessment system which is thereafter enabled to evaluate free-response text answers in response to examination prompts using discriminative preference ranking of predetermined linguistic text features, the configuring including generating a trained model weight vector for subsequent use in automatically evaluating said free-response text answers, by: accessing a plurality of training linguistic vectors (x 1 , x 2 , x 3 ,. .. x n ), each training linguistic vector comprising a plurality of numerical values representing predetermined linguistic features of text comprising sentences within a training text, at least some of said predetermined linguistic features representing at least one of lexical, part-of-speech or parsing of words within said sentences; accessing, for each of a plurality of predetermined pairs of said training linguistic vectors (x i , x j ), predetermined ranking data (r i , r j ) that defines which one of the pair of training linguistic vectors (x i , x j ) is representative of a better training text; accessing an initial weight vector (w i ) comprising a plurality of numerical weights corresponding to the plurality of numerical values in the training vectors; generating pairwise difference training vectors for a plurality of ranked pairs of training vectors (x j −x i ) each pairwise difference training vector being calculated as a difference between a ranked pair of said training linguistic vectors; and performing an iterative process to adapt said initial weight vector (w i ) to a trained model weight vector (w m ) by: i) calculating a dot product between a current weight vector and each pairwise difference training vector to generate a respective scalar value for each pairwise difference training vector; ii) determining, for each pairwise difference training vector, if the current weight vector misclassified the pairwise difference training vector in dependence upon a comparison result obtained by comparing the scalar value for the pairwise difference training vector with a predetermined threshold; iii) generating an aggregate vector (ã) by summing the pairwise difference training vectors that said determining step determines are misclassified and normalizing the summed result with a current timing factor; iv) calculating a new weight vector by arithmetically combining numerical values of the current weight vector with respectively corresponding numerical values of the generated aggregate vector; and v) repeating steps i) through iv) until the current timing factor reaches a predetermined condition, whereupon the then current weight vector becomes said trained model weight vector (w m ); and (B) subsequently using said trained model weight vector to automatically evaluate the linguistic quality of each of plural input free-text answers submitted for evaluation by: generating a linguistic vector for an input free-text answer that is to be evaluated; calculating, using a processor, a dot product between the trained model weight vector and the linguistic vector for the input free-text answer that is to be evaluated to generate a scalar value for the input free-text answer; and outputting an evaluation of the input free-text answer using the scalar value generated for the input free-text answer.