Graph-based Event Coreference Resolution

1. Graph-based Event Coreference Resolution Zheng Chen and Heng Ji City University of New York August 7, 2009

2. Outline • Event Coreference Resolution Task • Graph-based event coreference resolution • Spectral Graph Clustering • Coreference Matrix Construction • Global Weights • Maximum Entropy based • Experiments and Analysis • Conclusion and Future Work TextGraphs 2009

3. Event Coreference Resolution Task • Grouping all the event mentions into equivalence classes so that all the mentions in each class refer to a unified event (33 event types defined in ACE program) 4. Ankara police chief Ercument Yilmaz visited the site of the morningblast 1.An explosion in a cafe at one of the capital's busiest intersections killed one woman and injured another Tuesday 2. Police were investigating the cause of the explosion inthe restroom of the multistory Crocodile Cafe in the commercial district of Kizilayduring the morning rush hour 5. The explosion comes a month after 6. a bomb exploded at a McDonald's restaurant in Istanbul, causing damage but no injuries 7.Radical leftist, Kurdish and Islamic groups are active in the country and have carried out the bombing in the past 3. The blast shattered walls and windows in the building TextGraphs 2009

4. Spectral Graph Clustering 4 TextGraphs 2009

5. Spectral Graph Clustering cut(A,B) = 0.1+0.2+0.2+0.3=0.8 0.8 A 0.7 0.9 0.8 0.9 0.6 0.3 0.8 0.2 0.7 0.2 B 0.1 5 TextGraphs 2009

6. Spectral Graph Clustering (Cont’) • Start with full connected graph, each edge is weighted by the coreference value • Optimize the normalized-cut criterion (Shi and Malik, 2000) • vol(A): The total weight of the edges from group A • Maximize weight of within-group coreference links • Minimize weight of between-group coreference links 6 TextGraphs 2009

7. Two Methods for Computing Coreference Matrix Method 1: Computing global weights • Compute 16 types of weights (8 trigger related and 8 argument related) based on a training corpus • An exponential function to incorporate the 16 weights Method 2: Applying a Maximum Entropy Model • Learn a Maximum Entropy Model using trigger/distance/argument related features 7 TextGraphs 2009 TextGraphs 2009

8. Method1: Global Weights Coreferential Non-Coreferential entity and role match entity match, role mismatch both role and entity don’t match role match but entity doesn’t 8 TextGraphs 2009

9. Method 2: Maximum Entropy Model 9 TextGraphs 2009

10. Experiments • 560 ACE05 documents, 10-folder cross-validation • Scoring metric: ECM F-Measure (Luo, 2005) • Best threshold 0.85 for both methods • Method1: 83.6% and Method2: 83.1% on test set • Method1: better resolve conflictions • Method2: handle long-distance event mentions better 10 TextGraphs 2009

11. Conclusion Related Work MUC event coreference (e.g. Humphreys et al., 1997; Bagga and Baldwin, 1999) in MUC was limited to several scenarios Graph-based entity coreference resolution (Nicolae and Nicolae, 2006; Ng, 2009) The first effort to apply graph-based algorithm to the problem of event coreference resolution Future Work: Extend to cross-document event coreference resolution 11 TextGraphs 2009 TextGraphs 2009

12. This work is supported by the Defense Advanced Research Projects Agency under Contract No. HR0011-06-C-0023, Google Research, CUNY Research Enhancement Program and GRTI Program Thank you 12 TextGraphs 2009