12. The method of claim 1 , comprising training the scoring model using a plurality of training texts, wherein each training text is associated with a human assigned coherence score.
1. A computer-implemented method of automatically generating a coherence score for a text using a scoring model, comprising: identifying a plurality of lexical chains within a text to be scored with a processing system, wherein a lexical chain comprises a set of words spaced within the text, certain words in the lexical chain being non-contiguous; identifying a discourse element within the text with the processing system based on lookup operations with a computer database of discourse elements, wherein the discourse element comprises a word within the text; determining a coherence metric with the processing system based on a relationship between the lexical chains and the discourse element; wherein determining the coherence metric comprises determining: a first count of lexical chains that end before the discourse element; a second count of lexical chains that begin after the discourse element; and a third count of lexical chains that begin before and end after the discourse element; wherein the coherence metric is determined based on the first count, the second count, and the third count; generating a coherence score using the processing system by applying a scoring model to the coherence metric, wherein the scoring model comprises multiple weighted features whose feature weights are determined by training the scoring model relative to a plurality of training texts, the coherence score being combined with other non-lexical chain features by an automated essay evaluation engine to determine a transmitted quality level of the text.