Coreference resolution systems rely heavily on string overlap (e.g., Google Inc. and Google), performing badly on mentions with very different words (opaque mentions) like Google and the search giant. Yet prior attempts to resolve opaque pairs using ontologies or distributional semantics hurt precision more than improved recall. We present a new unsupervised method for mining opaque pairs. Our intuition is to restrict distributional semantics to articles about the same event, thus promoting referential match. Using an English comparable corpus of tech news, we built a dictionary of opaque coreferent mentions (only 3% are in WordNet). Our dictionary can be integrated into any coreference system (it increases the performance of a state-of-the-art system by 1% F1 on all measures) and is easily extendable by using news aggregators.
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