Efficient Collective Entity Linking with Stacking

Entity disambiguation works by linking am-biguous mentions in text to their correspond-ing real-world entities in knowledge base. Re-cent collective disambiguation methods en-force coherence among contextual decisions at the cost of non-trivial inference processes. We propose a fast collective disambiguation approach based on stacking. First, we train a local predictor g0 with learning to rank as base learner, to generate initial ranking list of can-didates. Second, top k candidates of related instances are searched for constructing expres-sive global coherence features. A global pre-dictor g1 is trained in the augmented feature space and stacking is employed to tackle the train/test mismatch problem. The proposed method is fast and easy to implement. Exper-iments show its effectiveness over various al-gorithms on several public datasets. By learn-ing a rich semantic relatedness measure be-tween entity categories and context document, performance is further improved.