To assign a probability to each hyperedge, we can first obtain a positive number for a hyperedge using the scores of the corresponding edges: 1 c(e) = exp v∈tails(e) s v, head(e) |tails(e)| where c(e) is the count of a hyperedge e, head(e) is a head, tails(e) is a set of dependants of the head, v is one dependant, and s(v, head(e)) is the score of an edge from v to head(e).
for example, the count of the hyperedge e 1 in figure 3(c) is Then, the probability of a hyperedge can be obtained by normalizing the count among all hyperedges with the same head collected from a training corpus: c(e) e :head(e )=head(e) c(e ) we obtain a weighted dependency forest in which each hyperedge has a probability.