Intuitively, we would like to assign a KB constant that has high similarity with words the slot is aligned to.
we define s local of a KB constant c i for every slot y i to be its expected semantic similarity under the alignment distribution: We define sim φ (x j , c i ) to be the cosine similarity between the embedding φ(x j ) and the embedding φ(c i ) (scaled to the range [0,1]), where φ(c i ) is defined to be the average embedding of all words in L( Global Score Utilizing only a local scoring function raises several concerns.