Deep Ranking Based Cost-sensitive Multi-label Learning for Distant Supervision Relation Extraction
Table 1: The proportions of NR samples from Riedel’s dataset.
['[BOLD] Pro. (%)', '[BOLD] Training', '[BOLD] Test']
In relation extraction, the dataset will always contain certain negative samples which do not express any relation types and are classified as NR type (no relation). In order to relieve this problem, we adopt cost-sensitive learning to construct the loss function. Based on G[att], the cost-sensitive loss function which is Variant-3 is as follows: G[cost_att]=∑c∗∈Lk{g(c∗)(ρ[0,σ+−F(rc∗,c∗)]) +ρ[0,σ−+F(rc∗,c−)] +∑c+∈Lk−c∗γρ[0,σ+−F(rc∗,c+)] +γ1(c∗≠cNR)ρ[0,σ−+F(rc∗,cNR)] } (20) where g(c∗)=1(c=cNR)λ+1(c≠cNR)1; 1(⋅) is an indicate function. Similar to Eq. c−=argmaxc∈L−LkF(rc∗,c) (21)