The interaction of lexical and structural information is modeled by means of corpus-based statistical techniques. Due to the impact of generative grammar on linguistics, collocations have been regarded as a phenomenon outside the grammar. In general, reduction of grammar to competence aspects has lead to grammar models that account for the dichotomy of syntactically correct versus incorrect utterances, but ignore the fact that some of the correct analyses are more adequate than others. In order to come up with efficient and sufficiently general systems we need to combine statistical models with elaborate linguistic knowledge. One possibility to achieve this goal is to provide corpora with linguistically elaborate annotation schemes.
Grammatical competence can also alleviate another inherent problem of statistical models.
Since the number of model parameters is limited by the size of the training corpus a linguistically guided pre-selection of appropriate candidate parameters is crucial. Within the project, stochastic grammars with different degrees of lexicalization will be induced from a German newspaper corpus. Parametrization of the grammar models is guided by insights gained from corpus-based retrieval of collocations. The initial model will be trained on annotated portions of the corpus. The parameters will be systematically varied and tested in a number of parsing experiments.