Abstract
This paper presents a novel method for automatically identifying the move structure in academic abstracts to assist non-native speaker of English in academic writing. In our approach, we use a small set of manually tagged abstracts as training corpus and analyze the significant features. Maximum Entropy model (ME) is employed to classify the move structure in the given abstracts. It involves automatically learning of the syntactic features, and automatically building a statistical model. The proposed method outperforms the previous research with a significantly higher accuracy. Our methodology clearly shows that the ME could suitably model the abstract structure, and implies that a more flexible move tagger can be easily applied to different research domains using a small set of manually tagged abstracts.