Data-driven systems for natural language processing have the advantage that they can easily be ported to any language or domain for which appropriate training data can be found. However, many data-driven systems require careful tuning in order to achieve optimal performance, which may require specialized knowledge of the system. We present MaltOptimizer, a tool developed to facilitate optimization of parsers developed using MaltParser, a data-driven dependency parser generator. MaltOptimizer performs an analysis of the training data and guides the user through a three-phase optimization process, but it can also be used to perform completely automatic optimization. Experiments show that.
