German Chunker
The chunker for German was developed by Helmut Schmid and Sabine Schulte im Walde. It is based on a German Head-Lexicalised Probabilistic Context-Free Grammar. The manually developed grammar was semi-automatically extended by robustness rules in order to allow parsing of unrestricted text. The model parameters were learned from unlabelled training data by the probabilistic parser LoPar. You can download the chunker here.Predicate Argument Clustering (PAC)
The clustering approach was developed within the SFB-732 project D4 (Modular Lexicalization of Probabilistic Context-Free Grammars). PAC provides a cluster analysis for verb-frame-argument tuples of varying argument numbers, and incorporates a generalisation of the arguments by WordNet-based selectional preferences. You can download the software here.References:
Sabine Schulte im Walde, Helmut Schmid, Wiebke Wagner, Christian Hying, Christian Scheible
A Clustering Approach to Automatic Verb Classification incorporating Selectional Preferences: Model, Implementation, and User Manual
[pdf/bib/url]
SinSpeC: Working Papers of the SFB 732 "Incremental Specification in Context", Volume 7, December 2010.
Sabine Schulte im Walde, Christian Hying, Christian Scheible, Helmut Schmid
Combining EM Training and the MDL Principle for an Automatic Verb Classification incorporating Selectional Preferences
[pdf/bib]
Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics. Columbus, OH, June 2008.
Helmut Schmid, Sabine Schulte im Walde
Robust German Noun Chunking with a Probabilistic Context-Free Grammar
[pdf/bib]
Proceedings of the 18th International Conference on Computational Linguistics. Saarbrücken, Germany, August 2000.