Freely available statistical parsers often require careful optimization to produce state-of-the-art results, which can be a non-trivial task especially for application developers who are not interested in parsing research for its own sake. We present MaltOptimizer, a freely available tool developed to facilitate parser optimization using the open-source system MaltParser, a data-driven parser-generator that can be used to train dependency parsers given treebank data. MaltParser offers a wide range of parameters for optimization, including nine different parsing algorithms, two different machine learning libraries (each with a number of different learners), and an expressive specification language that can be used to define arbitrarily rich feature models. MaltOptimizer is an interactive system that first performs an analysis of the training set in order to select a suitable starting point for optimization and then guides the user through the optimization of parsing algorithm, feature model, and learning algorithm.
MaltOptimizer is developed by Miguel Ballesteros from Complutense University of Madrid (past) and Pompeu Fabra University (current) (Spain) and Joakim Nivre from Uppsala University (Sweden).
The current version of MaltOptimizer can be downloaded from the MaltOptimizer download page.
Check the new implementation for the Shared Task on Parsing Morphologically Rich Languages: SPMRL implementation.