In this paper, we propose a semi-automatic tree annotating workbench for building a Korean treebank. Generally, building a treebank requires an enormous effort by the annotator. In order to improve annotating efficiency, decrease the number of intervention required by the annotator, and help maintain consistent annotation in building a treebank, we have developed a semi-automatic tree annotating workbench consisting of following three stages: syntactic pattern extraction, syntactic pattern selection, and syntactic pattern application. The experiment was carried out with 27,966 tree tagged sentences as a training set and 3,108 sentences as a test set. As a result, the burden of manual annotation can be reduced by about 47% with the best selection of the feature set by using the proposed tree annotating workbench.