Transformation-based tectogrammatical analysis of Czech

Abstract

There are several tools that support manual annotation of data at the Tectogrammatical Layer as it is defined in the Prague Dependency Treebank. Using transformation-based learning, we have developed a tool which outperforms the combination of existing tools for pre-annotation of the tectogrammatical structure by 29% (measured as a relative error reduction) and for the deep functor (i.e., the semantic function) by 47%. Moreover, using machine-learning technique makes our tool almost independent of the language being processed. This paper gives details of the algorithm and the tool. © Springer-Verlag Berlin Heidelberg 2006.