110. The system as recited in claim 107 , wherein the one or more discourse comprehension features further comprise the number of iterations each phrase remains in the short-term memory (CI shortterm), CI shortterm normalized by the frequency of each phrase (CI normalized), or the maximum centrality score that each phrase achieves across the entire document (CI maxscore).
107. The system as recited in claim 94 , wherein the one or more discourse comprehension features are derived by the steps of: reading a sentence from the electronic document; extracting noun phrases from the sentence; constructing a graph in a working memory with nodes corresponding to the noun phrases; adding nodes to the graph for all phrases that are in a short-term memory; connecting all nodes with edges weighted by a semantic relatedness measure derived using a semantic memory; decreasing the weights on incoming and outgoing edges of nodes coming from the short-term memory by a decay factor proportional to the number of sentences since the phrase was last seen; ranking the nodes in the graph using a graph centrality measure; selecting a predetermined number of the highest-ranked nodes and placing their associated phrases in short-term memory; saving parameters related to a phrase ranking using the centrality measure and a number of iterations that the phrase remains in the short-term memory; repeating the above steps of reading, extracting, constructing, adding, connecting, decreasing, ranking, selecting and saving until all sentences have been read from the electronic document; and calculating the one or more discourse comprehension features using the saved parameters.