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Building an Event Extractor with Only a Few Examples
Pengfei Yu
, Zixuan Zhang
, Clare Voss
, Jonathan May
,
Heng Ji
National Center for Supercomputing Applications (NCSA)
Siebel School of Computing and Data Science
Coordinated Science Lab
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Keyphrases
Event-triggered
100%
Extractor
100%
Event Extraction
100%
Unsupervised Learning
66%
Event Type
66%
Extraction Model
66%
Argument Roles
66%
Source Code
33%
Model Use
33%
Publicly Available
33%
Training Data
33%
Human Effort
33%
Extraction System
33%
Human Cost
33%
Manual Labor
33%
Amount of Training
33%
Event Annotation
33%
Supervised Approach
33%
Zero-shot Learning
33%
Time-to-build
33%
Role Labeling
33%
Event Ontology
33%
Event Argument Extraction
33%
Unlabeled Corpus
33%
Supervision Approaches
33%
Trigger Detection
33%
Extraction pipeline
33%
Supervised Annotation
33%
Computer Science
Annotation
100%
Event Extraction
100%
Event-Type
66%
Training Data
33%
Research Purpose
33%
Ontology
33%
Substantial Amount
33%