Paper: Peeling Back the Layers: Detecting Event Role Fillers in Secondary Contexts

ACL ID P11-1114
Title Peeling Back the Layers: Detecting Event Role Fillers in Secondary Contexts
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2011
Authors

The goal of our research is to improve event extraction by learning to identify sec- ondary role filler contexts in the absence of event keywords. We propose a multi- layered event extraction architecture that pro- gressively “zooms in” on relevant informa- tion. Our extraction model includes a docu- ment genre classifier to recognize event nar- ratives, two types of sentence classifiers, and noun phrase classifiers to extract role fillers. These modules are organized as a pipeline to gradually zero in on event-related information. We present results on the MUC-4 event ex- traction data set and show that this model per- forms better than previous systems.