Paper: Efficient Collective Entity Linking with Stacking

ACL ID D13-1041
Title Efficient Collective Entity Linking with Stacking
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2013

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 effectiv...