學術論文摘要中文步結構的自動分析｜國立清華大學博碩士論文庫

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研究生：	盧昆玉 Kun-Yu Lu
論文名稱：	學術論文摘要中文步結構的自動分析 An Automatic Move Tagger for Academic Abstracts
指導教授：	張俊盛 Jason S. Chang
口試委員:
學位類別：	碩士 Master
系所名稱：	電機資訊學院 - 資訊系統與應用研究所 Institute of Information Systems and Applications
論文出版年：	2007
畢業學年度：	95
語文別：	英文
論文頁數：	83
中文關鍵詞：	學術論文寫作、摘要、文步結構、n-gram
外文關鍵詞：	Academic Writing, abstract, move structure, n-gram
相關次數：	點閱：3 下載：0
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本篇論文致力於研究學術論文摘要中文步結構的自動分析，藉以協助非以英語為母語的學者進行學術論文摘要寫作。我們的方法，能為每篇摘要中所有的句子都以各種不同語言特徵之文步序列進行自動標記。

此方法利用一組少量的人工標記文步的摘要群及n-grams，自動在大量未標記的摘要中訓練文步與n-grams之間的關係，以擴充產生可用來標示文步的顯著n-grams，並且使用已標記的摘要來訓練文步序列的馬可夫模型。以一組少量由人工標記的摘要做評估。最後，我們提供一個自動分析文步模型，能夠自動快速地把摘要標示上文步。

本篇研究結果顯示，我們的自動分析文步模型對於學術論文的摘要呈現了合理的精確性，並且快速的文步標記工具提供非以英語為母語的學者一個自動化、實用的方式，預期可以縮短了解或寫作學術論文中良好結構摘要的時間。

This paper presents a method for automatically labeling the move structures of academic abstracts to assist non-native speakers of English in writing academic abstracts.

In our approach, sentences in a given abstract are automatically labeled with move sequences. The method involves an annotating small set of abstracts and n-grams with moves by hand, and learning the relationships between moves and n-grams in large unlabeled abstracts, and training a Hidden Markov Model of move sequences. We also implement and evaluate an automatic move tagger.

The result of this paper shows that our automatic move tagger performs with reasonably high precision, and providing an automatic, practical and fast move tagger for non-native speakers to understand or write well-structured abstracts.

摘要    i
ABSTRACT    ii
Acknowledgement    iii
Table of Contents    iv
List of Tables    vi
List of Figures    vii
Chapter 1  Introduction    1
1.1  Motivation    1
1.2  Organization of the Thesis    4
Chapter 2  Related Work    5
2.1  Macrostructure of RAs    5
2.2  Linguistic Features and Rhetorical Structure of Abstracts in RAs    7
2.3  Computerized Corpus Analysis of Moves    9
Chapter 3  Method    10
3.1  Problem Statement    10
3.2  Procedure of Learning One Move per Sentence    11
3.2.1  Collect Abstracts for Training    12
3.2.2  Manually Label of Seed Abstracts with Moves    14
3.2.3  Extract Bigrams from U    16
3.2.4  Label Bigrams Based on One-Move-per-Bigrams Constraint    20
3.2.5  Automatically Extract Bigrams from Labeled Sentences in All Sections    21
3.2.6  Automatically Expand Bigrams with Moves    23
3.2.7  Build a Hidden Markov Model for Move Tagging    27
3.3  Automatic Move-tagging at Run-Time    29
3.3.1  Pre-processing    29
3.3.2  Extract Bigrams from TA    30
3.3.3  Use the Proposed HMM to Automatically Label TA    32
Chapter 4  Experiments and Evaluation    34
4.1  Experimental Setting    34
4.2  Evaluation    39
4.2.1  Evaluation Metrics    39
4.2.2  Analysis of Performance    39
Chapter 5  Discussion    42
5.1  Comparison with MOVER    42
5.2  Comparison with Su (2005)    43
5.3  Analysis of Errors    43
Chapter 6  Conclusion and Future Work    45
6.1  Overview    45
6.2  Future Work    45
References    47
Appendix A – Labeled Abstracts    51

                                

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