20. An automated computerized script assessment system for evaluating the linguistic quality of free-response text answers submitted by students in response to examination prompts, the system comprising: a computing processor configured 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 configured processor 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 training text scripts, 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 script; accessing an initial weight vector (w i ) comprising a plurality of numerical weights corresponding to the plurality of numerical values in the training linguistic 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 weight vector comprising: 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 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 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.
20. An automated computerized script assessment system for evaluating the linguistic quality of free-response text answers submitted by students in response to examination prompts, the system comprising: a computing processor configured 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 configured processor 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 training text scripts, 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 script; accessing an initial weight vector (w i ) comprising a plurality of numerical weights corresponding to the plurality of numerical values in the training linguistic 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 weight vector comprising: 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 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 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. 21. The system of claim 20 , wherein said iterative process further comprises calculating a loss factor that depends on the number of difference training vectors said determining determines are misclassified during a current iteration.