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

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...

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Bibliographic Details
Published inNatural language engineering Vol. 13; no. 2; pp. 95 - 135
Main Authors NIVRE, JOAKIM, HALL, JOHAN, NILSSON, JENS, CHANEV, ATANAS, ERYİGİT, GÜLŞEN, KÜBLER, SANDRA, MARINOV, SVETOSLAV, MARSI, ERWIN
Format Journal Article
LanguageEnglish
Published Cambridge, UK Cambridge University Press 01.06.2007
Subjects
Accuracy
Algorithms
Chinese
Computational linguistics
Computer Applications
Computer Generated Language Analysis
Datorlingvistik
Dependency Grammar
Germanic Languages
HUMANIORA och RELIGIONSVETENSKAP
HUMANITIES and RELIGION
Italian
Language
Languages and linguistics
Linguistic subjects
Lingvistikämnen
Machine learning
Natural language
Natural Language Processing
Parsing
Slavic Languages
Språkvetenskap
Systems engineering
Tree Structures
Turkish
Istanbul Turkey
Sweden
Turkey
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Summary: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.
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ISSN:1351-3249
1469-8110
DOI:10.1017/S1351324906004505