In order to overcome such limitation, we propose to model all possible non-consecutive k-grams in the trigger candidate, leading to the following set of non-consecutive k-gram vectors: The non-consecutive CNN model then follows the procedure of the traditional CNN model in Section 2.1 to compute the representation vector for classification.
the only difference is that the computation is done on the input set N instead of C. the convolution score set in this case would be S(N ) = {f t v : v 2 N }, while the aggregating score would be: Note that the maximum operation in Equation 2 only requires O(n) operations while the naive implementation of Equation 3 would need O(|N |) = O(n k ) operations.