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

Ruihong Huang and Ellen Riloff
University of Utah


Abstract

The goal of our research is to improve event extraction by learning to identify secondary role filler contexts in the absence of event keywords. We propose a multi-layered event extraction architecture that progressively ``zooms in'' on relevant information. Our extraction model includes a document genre classifier to recognize event narratives, 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 extraction data set and show that this model performs better than previous systems.




Full paper: http://www.aclweb.org/anthology/P/P11/P11-1114.pdf