Year: 2006
Type: conference-paper
Abstract: Complex tasks like question answering need to be able to identify events in text and the relations among those events.We show that this event identification task and a related task, identifying the semantic class of these events, can both be formulated as classification problems in a word-chunking paradigm.We introduce a variety of linguistically motivated features for this task and then train a system that is able to identify events with a precision of 82% and a recall of 71%.We then show a variety of analyses of this model, and their implications for the event identification task.
Authors: Steven Bethard, James H. Martin
Institution: University of Colorado Boulder
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
Awards: IMPROVED TRANSPORT PROCESSES FOR CCSM, G222768804, MRI: Collaborative Research: Acquisition of an IBM BG/L Supercomputer, Academic Research Infrastructure: Acquisition of a Grand Challenge Data Laboratory, IMPLEMENTING AN EFFICIENT SUPEER COMPUTER BASED EN GINE FOR END TO END OPERATION OF A HIGH RESOLUTIO
OpenAlex ID: W2004803990