SYDNEY CMCRC at TAC 2013

We use a supervised whole-document approach to English Entity Linking with simple clustering approaches. The system extends our TAC 2012 system (Radford et al., 2012), introducing new features for modelling local entity description and type-specific matching as well type-specific supervised models and supervised NIL classification. Our rule-based clustering takes advantage of local description and topics to split NIL clusters. The best system uses supervised entity linking and local description type clustering and scores 72.7% B+ F1 score. Our KB clustering score is competitive with the top system at 71.4%.