This paper demonstrates that machine learning is a suitable approach for rapid parser development. From 1000 newly treebanked Korean sentences we generate a deterministic shift-reduce parser. The quality of the treebank, particularly crucial given its small size, is supported by a consistency checker. 1 Introduction Given the enormous complexity of natural language, parsing is hard enough as it is, but often unforeseen events like the crises in Bosnia or East-Timor create a sudden demand for parsers and machine translation systems for languages that have not benefited from major attention of the computational linguistics community up to that point. Good machine translation relies strongly on the context of the words to be translated, a context that often goes well beyond neighboring surface words. Often basic relationships, like that between a verb and its direct object, provide crucial support for translation. Such relationships are usually provided by parsers. The NLP resources f...