As a result, for a candidate pair (n, n ) and a knowledge source i, its knowledge attention score is computed via where o i n,p = [e n , k i n,p ] and o i n ,p = [e n , k i n ,p ] are the concatenation of span representation and external knowledge embedding for candidate n and n respectively.
the weight for features from different knowledge sources is thus computed via Similar to the weighting component, for each feature i, we compute its score f i k (n, n , p) for n against n in the scoring component through where it is worth noting that we exclude e in this component for the reason that, in practice, the dimension of e is normally much higher than k. it could dominate the computation if e and k is concatenated.