For efficiency's sake, we use only spans of length less than 15; approximately 95% of the arguments in our dataset were within 15 words of the predicate, so even with this restriction we are able to supply features for nearly all valid arguments.
the second step of our training procedure is to create a separate data point for each span of S. For each span t ∈ Spans(S), we construct a Span-HMM with a regular node generating each element of S, except that a span node generates all of t. our training data contains many different copies of each sentence S, with a different Span-HMM generating each copy.