Description: MaltParser is a system for data-driven dependency parsing, which can be used to induce a parsing model from treebank data and to parse new data using an induced model.
Keywords: MaltParser, dependency parsing, Nivre, NLP, CoNLL, treebank, machine learning, data-driven, parsing

# MaltParser

Modified: July 15 2010

- Home

##### Get MaltParser

- Download
- Changes
- License

##### Documentation

- Introduction
- Installing MaltParser
- User guide
- Options (short version)
- Options (long version)
- JavaDoc

##### Resources

- Pre-trained models
- Plugin
- Publications
- CoNLL Shared Task

##### Contact

- Contact

MaltParser is a system for data-driven dependency parsing, which can be used to induce a parsing model from treebank data and to parse new data using an induced model. MaltParser is developed by Johan Hall, Jens Nilsson and Joakim Nivre at Växjö University and Uppsala University, Sweden.

The latest version of MaltParser is available from the MaltParser download page.

NB: Parsers developed using MaltParser have achieved state-of-the-art accuracy for a number of languages. However, please note that MaltParser is a complex system with many parameters that need to be optimized. Simply using the system "out of the box" with default settings is likely to result in sub-optimal performance and should not be used as a reference in comparative parser evaluations (unless it is explicitly stated that it is a non-optimized version of the system). For many languages and data sets, information about optimized settings can be found in the literature or on the web. Please make the effort to use this information in order to avoid misleading comparisons.