This paper describes the rule-based approach for ambiguity resolution used by English sentence parser in E-TRAN 2001, an English to Korean machine translation system. Parser’s Ambiguity Type Information (PATI) is used to automatically identify the types of ambiguities observed in competing candidate trees produced by the parser and summarizes the types into a formal representation. PATI provides an efficient way of encoding knowledge into grammar rules and calculating rule preference scores from a relatively small training corpus. We compare the enhanced grammar with the initial one in view of the amount of ambiguity. The experimental results show that the rule preference scores could significantly increase the accuracy of ambiguity resolution.