MaltParser: A Data-Driven Parser-Generator for Dependency Parsing

Published 1 May 2006

Joakim Nivre, Johan Hall, Jens Nilsson

Source URL

Citations466

SJR quartileQ1

SJR score0.48

SNIP1.79

TL;DR

MaltParser supports several parsing algorithms and learning algorithms, and allows user-defined feature models, consisting of arbitrary combinations of lexical features, part-of-speech features and dependency features, for dependency parsing.

Abstract

We introduce MaltParser, a data-driven parser generator for dependency parsing. Given a treebank in dependency format, MaltParser can be used to induce a parser for the language of the treebank. MaltParser supports several parsing algorithms and learning algorithms, and allows user-defined feature models, consisting of arbitrary combinations of lexical features, part-of-speech features and dependency features. MaltParser is freely available for research and educational purposes and has been evaluated empirically on Swedish, English, Czech, Danish and Bulgarian. 1.

Keywords

Computer Science

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