We introduce a novel iterative approach for event coreference resolution that gradually builds event clusters by exploiting inter-dependencies among event mentions within the same chain as well as across event chains. Among event mentions in the same chain, we distinguish within- and cross-document event coreference links by using two distinct pairwise classifiers, trained separately to capture differences in feature distributions of within- and cross-document event clusters. Our event coreference approach alternates between WD and CD clustering and combines arguments from both event clusters after every merge, continuing till no more merge can be made. And then it performs further merging between event chains that are both closely related to a set of other chains of events. Experiments on the ECB+ corpus show that our model outperforms state-of-the-art methods in joint task of WD and CD event coreference resolution.
Different approaches, focusing on either of WD or CD coreference chains, have been proposed for event coreference resolution. Works specific to WD event coreference includes pairwise classifiers @cite_12 @cite_28 graph based clustering method @cite_18 , information propagation @cite_30 , and markov logic networks . As to only CD event coreference, created pairwise classifiers using features indicating granularities of event slots and in another work ( cybulska2015bag ), grouped events based on compatibilities of event contexts.
@cite_28 @cite_18 @cite_30 @cite_12
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In past years, there has been substantial work on the problem of entity coreference resolution whereas much less attention has been paid to event coreference resolution. Starting with some motivating examples, we formally state the problem of event coreference resolution in the ACE program, present an agglomerative clustering algorithm for the task, explore the feature impact in the event coreference model and compare three evaluation metrics that were previously adopted in entity coreference resolution: MUC F-Measure, B-Cubed F-Measure and ECM F-Measure. In this paper we focus on a new problem of event coreference resolution across television news videos. Based on the observation that the contents from multiple data modalities are complementary, we develop a novel approach to jointly encode effective features from both closed captions and video key frames. Experiment results demonstrate that visual features provided 7.2 absolute F-score gain on stateof-the-art text based event extraction and coreference resolution. Event detection and recognition is a complex task consisting of multiple sub-tasks of varying difficulty. In this paper, we present a simple, modular approach to event extraction that allows us to experiment with a variety of machine learning methods for these sub-tasks, as well as to evaluate the impact on performance these sub-tasks have on the overall task.