Many extensions to text-based, data-intensive knowledge management approaches, such as Information Retrieval or Data Mining, focus on integrating the impressive recent advances in language technology. For this, they need fast, robust parsers that deliver linguistic data which is meaningful for the subsequent processing stages. This paper introduces such a parsing system and discusses some of its disambiguation techniques which are based on learning from a large syntactically annotated corpus.
The paper is organized as follows. Section 2 explains the motivations for writing the parser, and why it profits from Dependency grammar assumptions. Section 3 gives a brief introduction to the parsing system and to evaluation questions. Section 4 presents the probabilistic models and the conducted experiments in detail.