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We introduce a novel approach for resolving coreference when the trigger word refers to multiple (sometimes non-contiguous) clauses. Our approach is completely unsupervised, and our experiments show that Neural Network models perform much better (about 20% more accurate) than traditional feature-rich baseline models. We also present a new dataset for Biomedical Language Processing which, with only about 25% of the original corpus vocabulary, still captures the essential distributional semantics of the corpus.

Unsupervised Event Coreference for Abstract Words