Using Hybrid Parsing Models as Predictors for a Symbolic Parser

We have presented an architecture for the fusion of information contributed from a variety of components which are either based on expert knowledge or have been trained on quite different data collections. The results of the experiments show that there is a high degree of synergy between these different contributions, even if they themselves are fairly unreliable. Integrating all the available predictors we were able to improve the overall labeled accuracy on a standard test set for German to 91.1%, a level which is as least as good as the results reported for alternative approaches to parsing German.