Automatically Identify Sections in Academic Abstracts

摘要：

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 Markov model (MEMM) is employed to classify the move and structure in the given abstracts. It involves automatically learning 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 MEMM 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.

作者: Ying-Hsiu Lin Mei-Hua Chen Jian-Cheng Wu Jason S Chang

作者单位: Institute of Information Systems and Applications National Tsing Hua University Department of Computer Science National Tsing Hua University

会议类型: 国际会议

会议名称: 2009 International Conference on Applied Linguistics & Language Teaching(2009应用语言学暨语言教学国际研讨会)

会议地点: 台湾

会议语种:英文

页码: 370-379

在线出版日期: 2009-04-01（万方平台首次上网日期，不代表论文的发表时间）