A Hierarchical Distance-dependent Bayesian Model for Event Coreference Resolution

A Hierarchical Distance-dependent Bayesian Model for Event Coreference Resolution Bishan Yang Claire Cardie Department of Computer Science Cornell University {bishan, cardie}@cs.cornell.edu Peter Frazier School of Operations Research and Information Engineering Cornell University pf98@cornell.edu Abstract We present a novel hierarchical distance- dependent Bayesian model for event coref- erence resolution. While existing generative models for event coreference resolution are completely unsupervised, our model allows for the incorporation of pairwise distances be- tween event mentions — information that is widely used in supervised coreference mod- els to guide the generative clustering process- ing 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 cluster- ing. Experiments on the ECB+ corpus show that our model outperforms state-of-the-art methods for both within- and cross-document event coreference resolution. 1 Introduction The task of event coreference resolution consists of identifying text snippets that describe events, and then clustering them such that all event mentions in the same partition refer to the same unique event. Event coreference resolution can be applied within a single document or across multiple documents and is crucial for many natural language process- ing tasks including topic detection and tracking, in- formation extraction, question answering and tex- tual entailment (Bejan and Harabagiu, 2010). More importantly, event coreference resolution is a neces- sary component in any reasonable, broadly applica- ble computational model of natural language under- standing (Humphreys et al., 1997). In comparison to entity coreference resolu- tion (Ng, 2010), which deals with identifying and grouping noun phrases that refer to the same dis- course entity, event coreference resolution has not been extensively studied. This is, in part, because events typically exhibit a more complex structure than entities: a single event can be described via multiple event mentions, and a single event mention can be associated with multiple event arguments that characterize the participants in the event as well as spatio-temporal information (Bejan and Harabagiu, 2010). Hence, the coreference decisions for event mentions usually require the interpretation of event mentions and their arguments in context. See, for example, Figure 1, in which five event mentions across two documents all refer to the same under- lying event: Plane bombs Yida camp. Event: Plane bombs Yida camp Document 1 Document 2 The {Yida refugee camp} {in South Sudan} was bombed {on Thursday}. The {Yida refugee camp} was the target of an air strike {in South Sudan} {on Thursday}. {Two bombs} fell {within the Yida camp}, including {one} {close to the school}. {At least four bombs} were reportedly dropped. {Four bombs} were dropped within just a few moments - {two} {inside the camp itself}, while {the other two} {near the airstrip}. Figure 1: Examples of event coreference. Mutually coreferent event mentions are underlined and in boldface; participant and spatio-temporal information for the high- lighted event is marked by curly brackets. Most previous approaches to event coreference resolution (e.g., Ahn (2006), Chen et al. (2009)) op- erated by extending …

Content truncated to 500 words