Publication at Faculty of Mathematics and Physics |
2009
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.