Informed ways of improving data-driven dependency parsing for German
Wolfgang Seeker, Bernd Bohnet, Lilja Øvrelid, Jonas Kuhn · International Conference on Computational Linguistics · 2010
We investigate a series of targeted modifications to a data-driven dependency parser of German and show that these can be highly effective even for a relatively well studied language like German if they are made on a (linguistically and methodologically) informed basis and with a parser implementation that allows for fast and robust training and application. Making relatively small changes to a range of very different system components, we were able to increase labeled accuracy on a standard test set (from the CoNLL 2009 shared task), ignoring gold standard part-of-speech tags, from 87.64% to 89.40%. The study was conducted in less than five weeks and as a secondary project of all four authors. Effective modifications include the quality and combination of auto-assigned morphosyntactic features entering machine learning, the internal feature handling as well as the inclusion of global constraints and a combination of different parsing strategies.