Dependency parsing is considered a key technology for improving information extraction tasks.
Research indicates that dependency parsers spend more than 95\% of their total runtime on feature computations.
Based on this insight, this paper investigates the potential of improving parsing throughput by
designing feature representations which are optimized for combining single features to more complex feature templates and by optimizing parser constraints.
Applying these techniques to MDParser increased its throughput four fold, yielding Syntactic Parser, a dependency parser that outperforms comparable approaches by factor 25 to 400.
Keywords: natural language processing, dependency parsing, performance optimization, throughput