Korean is a morphologically rich language in which grammatical functions are marked by inflections and affixes, and they can indicate grammatical relations such as subject, object, predicate, etc. A Korean sentence could be thought as a sequence of eojeols. An eo- jeol is a word or its variant word form ag- glutinated with grammatical affixes, and eo- jeols are separated by white space as in En- glish written texts. Korean treebanks (Choi et al., 1994; Han et al., 2002; Korean Lan- guage Institute, 2012) use eojeol as their fun- damental unit of analysis, thus representing an eojeol as a prepreterminal phrase inside the constituent tree. This eojeol-based an- notating schema introduces various complex- ity to train the parser, for example an en- tity represented by a sequence of nouns will be annotated as two or more different noun phrases, depending on the number of spaces used. In this paper, we propose methods to transform eojeol-based Korean treebanks into entity-based Korean treebanks. The methods are applied to Sejong treebank, which is the largest constituent treebank in Korean, and the transformed treebank is used to train and test various probabilistic CFG parsers. The experi- mental result shows that the proposed transfor- mation methods reduce ambiguity in the train- ing corpus, increasing the overall F1 score up to about 9 %.