Informed ways of improving data-driven dependency parsing for German

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 partof-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 autoassigned morphosyntactic features entering machine learning, the internal feature handling as well as the inclusion of global constraints and a combination of different parsing strategies.

• Type: Journal
• Year: 2010
• Where: COLING
• Authors: Wolfgang Seeker, Bernd Bohnet, Lilja Øvrelid, Jonas Kuhn
• Keywords: COLING 2010, Computational Linguistics, Data-driven Dependency Parser, Features Entering Machine, Standard Partof-speech Tags