Part of Advances in Neural Information Processing Systems 22 (NIPS 2009)
Cosmin Bejan, Matthew Titsworth, Andrew Hickl, Sanda Harabagiu
We present a sequence of unsupervised, nonparametric Bayesian models for clustering complex linguistic objects. In this approach, we consider a potentially infinite number of features and categorical outcomes. We evaluate these models for the task of within- and cross-document event coreference on two corpora. All the models we investigated show significant improvements when compared against an existing baseline for this task.