在本論文中，我們利用督導式機器學習方法，經由自動抽取論文摘要中重要的特徵值，自動建立文步標示與特徵值間的關係並以此訓練機器學習模型。以此機器學習模型可以自動標示論文摘要中的文步結構。
在訓練階段，我們利用少量人工標記文步的論文摘要，從中取出重要的特徵值，再訓練機器學習模型學習特徵值與文步之間的關係，以進行對論文摘要的自動文步分析。在執行階段，應用訓練好的機器學習模型，我們分別針對電腦科學領域與應用語言學領域標示研究者所撰寫的論文摘要文步，我們利用人工評估的方法，在兩個不同領域下，皆可達到平均超過８０%的正確率，顯示本方法可以成功標記論文摘要文步。

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.

摘要 I
ABSTRACT II
ACKNOWLEDGEMENT III
TABLE OF CONTENTS IV
CHAPTER 1 INTRODUCTION 1
CHAPTER 2 RELATED WORK 5
2.1 MACROSTRUCTURE OF RAS 5
2.2 STRUCTURAL REPRESENTATION OF ABSTRACTS 7
2.3 IDENTIFYING MOVES AS TEXT CLASSIFICATION 8
CHAPTER 3 METHOD 11
3.1 PROBLEM STATEMENT 11
3.2 LEARNING MOVE SENTENCE RELATION 13
3.2.1 Collecting Abstracts from the web 13
3.2.2 Manually label abstracts with moves 14
3.2.3 Select the features for machine learning 16
3.2.4 Train a machine learning model 20
3.3 RUN-TIME AUTOMATIC MOVE-TAGGING 22
CHAPTER 4 EXPERIMENTAL SETTING 25
4.1 EXPERIMENTAL SETTING 25
4.2 EVALUATION METRICS 27
CHAPTER 5 EVALUATION RESULTS 31
5.1 EVALUATION RESULTS 31
5.2 DISCUSSION AND ERROR ANALYSIS 35
CHAPTER 6 CONCLUSION AND FUTURE WORK 39
REFERENCES 40
APPENDIX A –GUIDELINES FOR HUMAN ANNOTATION OF ABSTRACT 43

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