In addition, to capture potential event sequence overlaps between new narratives and the already learned narratives, we build a verb bigram language model using verb sequences extracted from the learned narrative paragraphs and calculate the perplexity score (as a feature) of the verb sequence in a candidate narrative paragraph.
we calculate the perplexity score of an event sequence that is normalized by the number of events, P P (e 1 , ..., e N ) = N N i=1 1 P (e i |e i−1 ) , where N is the total number of events in a sequence and e i is a event word.