We seek answers to these questions by conducting a systematic evaluation of different world knowledge sources for learning-based coreference resolution.
we (1) derive world knowledge from encyclopedic sources that are underinvestigated for coreference resolution, including FrameNet (Baker et al., 1998) and YAGO (Suchanek et al., 2007), in addition to coreference-annotated data and unannotated data; (2) incorporate such knowledge as features into a richer baseline feature set that we previously employed (Rahman and Ng, 2009); and (3) evaluate their utility using two coreference models, the traditional mention-pair model (Soon et al., 2001) and the recently developed cluster-ranking model (Rahman and Ng, 2009).