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MaltParser: A language-independent system for data-driven dependency parsing

Published online by Cambridge University Press:  12 January 2007

JOAKIM NIVRE
Affiliation:
Växjö University, School of Mathematics and Systems Engineering, 35195 Växjö, SwedenUppsala University, Department of Linguistics and Philology, Box 635, 75126 Uppsala, Sweden e-mail: joakim.nivre@msi.vxu.se
JOHAN HALL
Affiliation:
Växjö University, School of Mathematics and Systems Engineering, 35195 Växjö, Sweden e-mail: johan.hall@msi.vxu.se, jens.nilsson@msi.vxu.se
JENS NILSSON
Affiliation:
Växjö University, School of Mathematics and Systems Engineering, 35195 Växjö, Sweden e-mail: johan.hall@msi.vxu.se, jens.nilsson@msi.vxu.se
ATANAS CHANEV
Affiliation:
University of Trento, Dept. of Cognitive Sciences, 38068 Rovereto, Italy ITC-irst, 38055 Povo-Trento, Italy e-mail: chanev@form.unitn.it
GÜLŞEN ERYİGİT
Affiliation:
Istanbul Technical University, Dept. of Computer Engineering, 34469 Istanbul, Turkey e-mail: gulsen.cebiroglu@itu.edu.tr
SANDRA KÜBLER
Affiliation:
University of Tübingen, Seminar für Sprachwissenschaft, Wilhelmstr. 19, 72074 Tübingen, Germany e-mail: kuebler@sfs.uni-tuebingen.de
SVETOSLAV MARINOV
Affiliation:
University of Skövde, School of Humanities and Informatics, Box 408, 54128 Skövde, SwedenGöteborg University & GSLT, Faculty of Arts, Box 200, 40530 Göteborg, Sweden e-mail: svetoslav.marinov@his.se
ERWIN MARSI
Affiliation:
Tilburg University, Communication and Cognition, Box 90153, 5000 LE Tilburg, The Netherlands e-mail: e.c.marsi@uvt.nl

Abstract

Parsing unrestricted text is useful for many language technology applications but requires parsing methods that are both robust and efficient. MaltParser is a language-independent system for data-driven dependency parsing that can be used to induce a parser for a new language from a treebank sample in a simple yet flexible manner. Experimental evaluation confirms that MaltParser can achieve robust, efficient and accurate parsing for a wide range of languages without language-specific enhancements and with rather limited amounts of training data.

Type
Papers
Copyright
2007 Cambridge University Press

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