Exploring two views of coreference resolution in a never-ending learning system

The first Never-Ending Learning system reported in the literature, which is called NELL (Never-Ending Language Learner), was designed to perform the task of autonomously building an knowledge base as a result of continuously reading the web. NELL is based on a learning paradigm in which, the learner, in an autonomous way, manages to constantly, incrementally and continuously evolve with time. But, most important than just keep evolving, in this paradigm acquired knowledge is used, in a dynamic way, to expand the scope and improve the performance of the learning task as a whole. Coreference resolution plays a key role in any system based on the Never-Ending Learning paradigm. In this paper two diferente views of correference resolution are applied to NELL's knowledge base and empirical evidence is obtained to show that combining morphological and semantic features in a hybrid model can be more effective than using only one of the feature views.