Paper: Same Referent, Different Words: Unsupervised Mining of Opaque Coreferent Mentions

ACL ID N13-1110
Title Same Referent, Different Words: Unsupervised Mining of Opaque Coreferent Mentions
Venue Annual Conference of the North American Chapter of the Association for Computational Linguistics
Session Main Conference
Year 2013
Authors

Coreference resolution systems rely heav- ily 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 at- tempts to resolve opaque pairs using ontolo- gies 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 se- mantics to articles about the same event, thus promoting referential match. Using an En- glish 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) an...