以成本敏感分類分析法建構之多語言文件分類技術

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研究生：	許偉忠 Wei-Chung Hsu
論文名稱：	以成本敏感分類分析法建構之多語言文件分類技術 Cost-Sensitive Poly-Lingual Text Categorization
指導教授：	魏志平 Chih-Ping Wei
口試委員:
學位類別：	碩士 Master
系所名稱：	科技管理學院 - 科技管理研究所 Institute of Technology Management
論文出版年：	2007
畢業學年度：	95
語文別：	英文
論文頁數：	46
中文關鍵詞：	文件探勘、文件分類、多語文件分類、文件翻譯、成本敏感度學習、統計雙語詞典
外文關鍵詞：	Text mining, Text categorization, Poly-lingual text categorization, Document translation, Cost-sensitive learning, statistical-based bilingual thesaurus
相關次數：	點閱：3 下載：0
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由於網際網路的興起以及全球化的趨勢，各種文件的取得變的容易，並且這些文件都用各種不同的語言所撰寫，越來越多的組織和個人必須具備處理多語文件的能力。假設這些組織與個人有大量的已分類多語文件，就可以利用這些文件，建立一個自動的多語分類系統。當獲得新的多語文件時就可以利用這個系統來做適合的分類。

但到目前為止，多語文件分類系統並不多見，並且已做出的系統分類準確度仍有待加強，因此本研究提出了一套以成本敏感分類分析法建構之多語言文件分類技術，本研究會將不同語言的文章透過統計雙語詞典做翻譯，並且給每篇翻譯文章一個翻譯成本(品質)，此成本為分類預測錯誤所獲得的成本，希望在分類錯誤成本最低的情況下，獲得最好的分類準確度。

本研究以特徵加強之多與文件分類技術作為我們的目標，在經過科學化的實驗方法後，證明本研究不論在中文或是英文文集都比目標較為傑出。

關鍵字：文件探勘，文件分類，多語文件分類，文件翻譯，成本敏感度學習，統計雙語詞典

Because of the trend of globalization, organizations and individuals often generate, acquire, and then archive documents written in different languages (i.e., poly-lingual documents). If organizations or individuals have already organized poly-lingual documents into their categories and would like to use this set of preclassified poly-lingual documents as training documents for constructing text categorization models that can classify newly arrived poly-lingual documents into appropriate categories, the organizations and individuals face the poly-lingual text categorization (PLTC) problem. Poly-lingual text categorization (PLTC) refers to the automatic learning of a text categorization model(s) from a set of preclassified training documents written in different languages and the subsequent assignment of unclassified poly-lingual documents to predefined categories on the basis of the induced text categorization model(s).Many text categorization techniques have been proposed in the literature; however, most of them deal with monolingual documents. In this study, we propose a cost-sensitive poly-lingual text categorization (CS-PLTC) technique that involves inclusion of translated documents to expand the training size for PLTC and use of cost-sensitive learning to reflect different qualities of training documents. Using the existing feature-reinforcement-based PLTC (FR-PLTC) technique as performance benchmarks, our empirical evaluation results show that our proposed CS-PLTC technique outperforms than the benchmark technique in both English and Chinese corpora.

Keywords: Text mining, Text categorization, Poly-lingual text categorization, Document translation, Cost-sensitive learning, statistical-based bilingual thesaurus

TABLE OF CONTENTS

誌謝辭    I
Abstract    II
中文摘要    III
TABLE OF CONTENTS    IV
LIST OF FIGURES    V
LIST OF TABLES    VI
Chapter 1 Introduction    1
1.1    Background    1
1.2    Research Motivation and Objective    3
1.3    Organization of the Thesis    5
Chapter 2 Literature Review    6
2.1    Monolingual Text Categorization Techniques    6
2.2    Existing Poly-Lingual Text Categorization Techniques    7
Chapter 3 Design of Cost-sensitive Poly-lingual Text Categorization (CS-PLTC) Technique    15
3.1    Bilingual Thesaurus Construction    16
3.2    Document Translation    19
3.3    Feature Selection    21
3.4    Misclassification Cost Estimation    23
3.5    Document Representation    24
3.6    Cost-Sensitive Learning    25
Chapter 4 Empirical Evaluation    27
4.1    Data Collection    27
4.2    Evaluation Procedure, Criteria, and Performance Benchmark    28
4.3    Parameter Tuning Experiments    29
4.4    Comparative Evaluations    33
4.5    Analysis of Effects of Cost-Sensitive Learning    35
Chapter 5 Conclusion and Future Research Directions    41
References    43





LIST OF FIGURES

Figure 2.1: Overall Process of the FR-PLTC Technique    8
Figure 2.2: Process of Categorization Learning (for Li)    9
Figure 2.3: Examples of Cross-lingual Associations    11
Figure 3.1: Overall Process of the CS-PLTC Technique    16
Figure 3.2: Process of Bilingual Thesaurus Construction    17
Figure 3.3: Process of Document Translation    20
Figure 3.4: Examples of Document Feature Translation    21
Figure 4.1: Effects of k and drw for CS-PLTC (Union) on English Corpus    30
Figure 4.2: Effects of k and drw for CS-PLTC (Reinforcement) on English Corpus    31
Figure 4.3: Effects of k and drw for CS-PLTC (Union) on Chinese Corpus    32
Figure 4.4: Effects of k and drw for CS-PLTC (Reinforcement) on Chinese Corpus    33
Figure 4.5: Effects of k and drw for NC-PLTC (Union) on English Corpus    36
Figure 4.6: Effects of k and drw for NC-PLTC (Reinforcement) on English Corpus    37
Figure 4.7: Effects of k and drw for NC-PLTC (Union) on Chinese Corpus    38
Figure 4.8: Effects of k and drw for NC-PLTC (Reinforcement) on Chinese Corpus    38









LIST OF TABLES

Table 3.1: An Example of A Partial Statistical-based Bilingual Thesaurus    19
Table 4.1: Summary of Document Corpora    28
Table 4.2: Document Distribution of English and Chinese Corpora    28
Table 4.3: Summary of Parameter Values Selected    33
Table 4.4: Performance Comparison of FR-PLTC and CS-PLTC on English Corpus    34
Table 4.5: Performance Comparison of FR-PLTC and CS-PLTC on Chinese Corpus    35
Table 4.6: Performance Comparison of NC-PLTC and CS-PLTC on English Corpus    39
Table 4.7: Performance Comparison of NC-PLTC and CS-PLTC on Chinese Corpus    39

                                

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