In this paper, we present improvements of our state-of-the-art concept tagger based on conditional random fields. Statistical models have been optimized for three tasks of varying complexity in three languages (French, Italian, and Polish). Modified training criteria have been investigated leading to small improvements. The respective corpora as well as parameter optimization results for all models are presented in detail. A comparison of the selected features between languages as well as a close look at the tuning of the regularization parameter is given. The experimental results show in what level the optimizations of the single systems are portable between languages.
Bibliographic reference. Hahn, Stefan / Lehnen, Patrick / Heigold, Georg / Ney, Hermann (2009): "Optimizing CRFs for SLU tasks in various languages using modified training criteria", In INTERSPEECH-2009, 2727-2730.