Random walks down the mention graphs for event coreference resolution

Abstract

Event coreference is an important task in event extraction and other natural language processing tasks. Despite its importance, it was merely discussed in previous studies. In this article, we present a global coreference resolution system dedicated to various sophisticated event coreference phenomena. First, seven resolvers are utilized to resolve different event and object coreference mention pairs with a new instance selection strategy and new linguistic features. Second, a global solution—a modified random walk partitioning—is employed for the chain formation. Being the first attempt to apply the random walk model for coreference resolution, the revised model utilizes a sampling method, termination criterion, and stopping probability to greatly improve the effectiveness of random walk model for event coreference resolution. Last but not least, the new model facilitates a convenient way to incorporate sophisticated linguistic constraints and preferences, the related object mention graph, as well as pronoun coreference information not used in previous studies for effective chain formation. In total, these techniques impose more than 20% F-score improvement over the baseline system.