In this paper, we present an unsupervised framework that bootstraps a complete corefer- ence resolution (CoRe) system from word as- sociations mined from a large unlabeled cor- pus. We show that word associations are use- ful for CoRe – e.g., the strong association be- tween Obama and President is an indicator of likely coreference. Association information has so far not been used in CoRe because it is sparse and difficult to learn from small labeled corpora. Since unlabeled text is readily avail- able, our unsupervised approach addresses the sparseness problem. In a self-training frame- work, we train a decision tree on a corpus that is automatically labeled using word associa- tions. We show that this unsupervised system has better CoRe performance than other learn- ing approaches that...

Click Here to View the Paper In XML Format