(2012) and Kozareva and Hovy (2010) where every taxonomic relation is treated equally, we assume the confidence of each taxonomic relation is different, depending on the amount of Algorithm 1 Taxonomy Induction Algorithm Input: T : the taxonomic relation set Output: V : the vertex set of resultant taxonomy; E: the edge set of resultant taxonomy; 1: Initialize V = {ROOT }, E = ∅; 2: for each taxonomic relation (t 1 ≫ t 2 ) ∈ T do 3: if ∄ e(t 3 , t 1 ) ∈ E with t 3 ̸ = ROOT then 11: if ∃ e(ROOT, t 2 ) ∈ E then 14: end if 16: end for 17: edgeWeighting(V, E); 18: graphPruning(V, E); evidence it has.
the hypernym graph edges will be weighted as follows: Note that the Score value in formula (6) is determined by the taxonomic relation identification process described in Section 2.2.4.