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

Natural language engineering, 2007-06, Vol.13 (2), p.95-135 [Peer Reviewed Journal]

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Title:
MaltParser: A language-independent system for data-driven dependency parsing
Author: NIVRE, JOAKIM ; HALL, JOHAN ; NILSSON, JENS ; CHANEV, ATANAS ; ERYİGİT, GÜLŞEN ; KÜBLER, SANDRA ; MARINOV, SVETOSLAV ; MARSI, ERWIN
Subjects: Computational linguistics ; Datorlingvistik ; HUMANIORA och RELIGIONSVETENSKAP ; HUMANITIES and RELIGION ; Languages and linguistics ; Linguistic subjects ; Lingvistikämnen ; Språkvetenskap
Is Part Of: Natural language engineering, 2007-06, Vol.13 (2), p.95-135
Description: 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.
Publisher: Cambridge, UK: Cambridge University Press
Language: English
Identifier: ISSN: 1351-3249
EISSN: 1469-8110
DOI: 10.1017/S1351324906004505
CODEN: NLENFE
Source: ProQuest One Psychology
AUTh Library subscriptions: ProQuest Central
Alma/SFX Local Collection

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