We present a novel hierarchical distance-dependent Bayesian model for event coreference resolution. While existing generative models for event coreference resolution are completely unsupervised, our model allows for the incorporation of pairwise distances between event mentions -- information that is widely used in supervised coreference models to guide the generative clustering processing for better event clustering both within and across documents. We model the distances between event mentions using a feature-rich learnable distance function and encode them as Bayesian priors for nonparametric clustering. Experiments on the ECB+ corpus show that our model outperforms state-of-the-art methods for both within- and cross-document event coreference resolution.
Coreference resolution in general is a difficult natural language processing (NLP) task and typically requires sophisticated inferentially-based knowledge-intensive models @cite_23 . Extensive work in the literature focuses on the problem of entity coreference resolution and many techniques have been developed, including rule-based deterministic models (e.g. , , ) that traverse over mentions in certain orderings and make deterministic coreference decisions based on all available information at the time; supervised learning-based models (e.g. , , ) that make use of rich linguistic features and the annotated corpora to learn more powerful coreference functions; and finally, unsupervised models (e.g. , Haghighi and Klein (2007, 2010)) haghighi2007unsupervised haghighi2010coreference that successfully apply generative modeling to the coreference resolution problem.
@cite_23
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Coreference resolution is a key problem in natural language understanding that still escapes reliable solutions. One fundamental difficulty has been that of resolving instances involving pronouns since they often require deep language understanding and use of background knowledge. In this paper we propose an algorithmic solution that involves a new representation for the knowledge required to address hard coreference problems, along with a constrained optimization framework that uses this knowledge in coreference decision making. Our representation, Predicate Schemas, is instantiated with knowledge acquired in an unsupervised way, and is compiled automatically into constraints that impact the coreference decision. We present a general coreference resolution system that significantly improves state-of-the-art performance on hard, Winograd-style, pronoun resolution cases, while still performing at the stateof-the-art level on standard coreference resolution datasets.