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.
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