However, simply training on the links in L may result in poor performance, because those links exhibit systematic differences from the mentions in M that the ranker will be applied to.
the reason is that our mention extractor attempts to populate M with all mentions, whereas L which contains only the specific subset of mentions that meet the hyperlinking conventions of Wikipedia, the features for M are distributed differently from those in L, and a model trained on L may not might not perform well on M. Our strategy is to leverage L to train an initial ranker, and then hand-label a small set of mentions from M to train a second-stage re-ranker that takes the ranking output of the initial ranker as a feature.