tailieunhanh - Báo cáo khoa học: "Peeling Back the Layers: Detecting Event Role Fillers in Secondary Contexts"

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 multilayered 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. | Peeling Back the Layers Detecting Event Role Fillers in Secondary Contexts Ruihong Huang and Ellen Riloff School of Computing University of Utah Salt Lake City UT 84112 huangrh riloff @ 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 multilayered 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. 1 Introduction Event extraction is an information extraction IE task that involves identifying the role fillers for events in a particular domain. For example the Message Understanding Conferences MUCs challenged NLP researchers to create event extraction systems for domains such as terrorism . to identify the perpetrators victims and targets of terrorism events and management succession . to identify the people and companies involved in corporate management changes . Most event extraction systems use either a learning-based classifier to label words as role fillers or lexico-syntactic patterns to extract role fillers from pattern contexts. Both approaches however generally tackle event recognition and role filler extraction at the same time. In other words 1137 most event extraction systems primarily recognize contexts that explicitly refer to a relevant event. For example a system that extracts information about murders will recognize expressions associated with murder . killed assassinated or shot to death and extract role fillers from the surrounding context. But many role fillers occur in contexts that do not .