Paper: Modeling Context in Scenario Template Creation

ACL ID I08-1021
Title Modeling Context in Scenario Template Creation
Venue International Joint Conference on Natural Language Processing
Session Main Conference
Year 2008

We describe a graph-based approach to Sce- nario Template Creation, which is the task of creating a representation of multiple re- lated events, such as reports of different hur- ricane incidents. We argue that context is valuable to identify important, semantically similar text spans from which template slots could be generalized. To leverage context, we represent the input as a set of graphs where predicate-argument tuples are ver- tices and their contextual relations are edges. A context-sensitive clustering framework is then applied to obtain meaningful tuple clus- ters by examining their intrinsic and extrin- sic similarities. The clustering framework uses Expectation Maximization to guide the clustering process. Experiments show that: 1) our approach generates high quality clus- ters...