Exploiting Synergies Between Open Resources for German Dependency Parsing, POS-tagging, and Morphological Analysis
Zurich Open Repository and Archive (University of Zurich)2013
Citations Over TimeTop 10% of 2013 papers
Abstract
We report on the recent development of ParZu, a German dependency parser. We discuss the effect of POS tagging and morphological analysis on parsing performance, and present novel ways of improving performance of the components, including the use of morphological features for POS-tagging, the use of syntactic information to select good POS sequences from an n-best list, and using parsed text as training data for POS tagging and statistical parsing. We also describe our efforts towards reducing the dependency on restrictively licensed and closed-source NLP resources.
Related Papers
- → BLEU(2001)21,090 cited
- → A Simple, Fast, and Effective Reparameterization of IBM Model 2(2018)839 cited
- Sequence to Sequence Learning with Neural Networks(2014)