Robust Natural Language Text Processing with of smes A brief

This is a brief description of using smes, a a robust and fast NL text processor and information extraction core system for German. A detailed description of smes can be found in [Neumann et al., 1997], [Neumann et al., 2000], and [Neumann and Piskorski, 2002]. Important properties of smes are

A Divide-and-Conquer Strategy for Shallow Parsing of German Free Texts

We present a divide-and-conquer strategy based on finite state technology for shallow parsing of realworld German texts. In a first phase only the topological structure of a sentence (i.e., verb groups, subclauses) are determined. In a second phase the phrased grammars are applied to the contents of the different fields of the main and sub-clauses. Shallow parsing is supported by suitably configured preprocessing, including: morphological and on-line compound analysis, efficient POS-filtering, and named entity recognition. The whole approach proved to be very useful for processing of free word order languages like German. Especially for the divide-andconquer parsing strategy we obtained an f-measure of 87.14% on unseen data