Hostname: page-component-848d4c4894-sjtt6 Total loading time: 0 Render date: 2024-06-24T14:03:49.363Z Has data issue: false hasContentIssue false

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

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)