Semi-supervised semantic role labeling

Semi-supervised semantic role labeling (2009)

by Hagen Fürstenau , Mirella Lapata

Venue:	IN PROCEEDINGS OF EACL
Citations:	4 - 0 self

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Datum	Value	Source
TITLE	Semi-supervised semantic role labeling	user correction
AUTHOR NAME	Hagen Fürstenau	user correction
AUTHOR AFFIL	Dept. of Computational Linguistics; Saarland University	user correction
AUTHOR ADDR	Saarbrücken, Germany	user correction
AUTHOR NAME	Mirella Lapata	user correction
AUTHOR AFFIL	School of Informatics; University of Edinburgh	user correction
AUTHOR ADDR	Edinburgh, UK	user correction
ABSTRACT	Large scale annotated corpora are prerequisite to developing high-performance semantic role labeling systems. Unfortunately, such corpora are expensive to produce, limited in size, and may not be representative. Our work aims to reduce the annotation effort involved in creating resources for semantic role labeling via semi-supervised learning. Our algorithm augments a small number of manually labeled instances with unlabeled examples whose roles are inferred automatically via annotation projection. We formulate the projection task as a generalization of the linear assignment problem. We seek to find a role assignment in the unlabeled data such that the argument similarity between the labeled and unlabeled instances is maximized. Experimental results on semantic role labeling show that the automatic annotations produced by our method improve performance over using hand-labeled instances alone.	user correction
YEAR	2009	user correction
VENUE	IN PROCEEDINGS OF EACL	user correction
VENUE TYPE	CONFERENCE	INFERENCE
CITATIONS	25 found	ParsCit 1.0