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研究生:王昱鈞
研究生(外文):Yu-Chun Wang
論文名稱:跨語言線上百科連結
論文名稱(外文):Cross-Language Encyclopedia Article Linking
指導教授:項潔項潔引用關係
指導教授(外文):Jieh Hsiang
口試委員:陳信希鄭卜壬高照明
口試委員(外文):Hsin-Hsi ChenPu-Jen ChengZhao-Ming Gao
口試日期:2015-07-23
學位類別:博士
校院名稱:國立臺灣大學
系所名稱:資訊工程學研究所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2015
畢業學年度:103
語文別:英文
論文頁數:114
中文關鍵詞:線上百科連結跨語言主題模型括號翻譯
外文關鍵詞:online encyclopedia article linkingcross-languagetopic modelparenthetical translation
相關次數:
  • 被引用被引用:0
  • 點閱點閱:118
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  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:2
線上百科全書(如維基百科等)已成為目前網路上最重要的內容服務之一。
將線上百科全書中不同語言的條目建立連結在多語知識庫的建置與整合上是一相當重要的課題,許多先前之相關研究主要著重在建立維基百科不同語言版本間之跨語言連結,然而維基百科於各個語言的條目涵蓋數量有相當顯著的差異,為解決此問題,將數個重要的不同語言之單語線上百科之條目建立其連結以建置一個跨語言線上百科全書已成為一個重要的研究課題。於本論文之中,我們定義了跨語言線上百科連結之研究問題,並提出一個利用雙語主題模型與相關翻譯內容為特徵之基於支持向量機的跨語言線上百科連結方法,將英文維基百科與中文百度百科之對應條目建立連結。為驗證我們所提出之方法的有效性,我們自中文百度百科與英文維基百科收集了一定數量之對應條目並以此建置了數個實驗資料集。實驗之數據顯示我們所提出之跨語言線上百科連結方法於平均倒數排名(MRR) 評估指標可達到0.8252,較基準系統高了0.1745 (+26.82%),其數據說明我們的方法在建立英中跨語言線上百科連結是相當有效的。我們的方法並非高度依賴語言之特性,可易於擴展應用於建立其它語言間之線上百科條目之連結。

Online encyclopedias, like Wikipedia, are one of the most widely used internet services around the world. Though Wikipedia has many language editions, their coverage is imbalanced when compared to the number of language users both online and offline. Furthermore, large alternative online encyclopedias exist for some languages, such as Chinese Baidu Baike. We could improve access to the knowledge in these various sources by constructing and integrating multiple online encyclopedias into large multilingual knowledge bases. The main task in such a project is creating links between articles in different encyclopedias in different languages. Most research to date has focused on linking articles in the different language editions of Wikipedia, yet little work has been done in linking other platform encyclopedias. In this thesis, we develop a method for cross-language encyclopedia article linking (CLEAL) between encyclopedias on different platforms, English Wikipedia and Chinese Baidu Baike. We use a bilingual topic model and translation features based on an SVM model to link articles between these two encyclopedias. To evaluate our approach, we compile datasets from Baidu Baike articles and their corresponding En Wikipedia articles. The evaluation results show that our approach achieves 0.8252 in MRR, outperforming the baseline system by 0.1745 (+26.82%). Our method does not heavily depend on specific platform formats or linguistic characteristics, so it could be easily extended to generate cross-language article links among other online encyclopedias in other languages and on other platforms.

口試委員會審定書 i
誌謝 ii
中文摘要 iii
英文摘要 iv
1 Introduction 1
2 Problem Statement 5
2.1 Articles in Online Encyclopedias . . . . . . . . . . . . . . . . . . . . . . 6
2.2 Formal Definition of CLEAL . . . . . . . . . . . . . . . . . . . . . . . . 7
2.3 Difference Between CLEAL and Document Alignment . . . . . . . . . . 8
2.4 Necessity of CLEAL . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
3 Related Work 16
3.1 Named Entity Translation . . . . . . . . . . . . . . . . . . . . . . . . . . 16
3.1.1 Automatic Extraction of Bilingual Translations . . . . . . . . . . 16
3.1.2 Parenthetical Translation Mining . . . . . . . . . . . . . . . . . . 18
3.2 Machine Transliteration . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
3.2.1 Phoneme-Based Machine Transliteration Methods . . . . . . . . 23
3.2.2 Grapheme-Based Machine Transliteration Methods . . . . . . . . 24
3.2.3 Hybrid Machine Transliteration Methods . . . . . . . . . . . . . 26
3.3 Cross-language Encyclopedia Article Linking . . . . . . . . . . . . . . . 27
3.3.1 Entity Linking . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
3.3.2 Cross-Language Entity Linking . . . . . . . . . . . . . . . . . . 28
3.3.3 Cross-Language Link Discovery . . . . . . . . . . . . . . . . . . 29
4 Named Entity Translation 31
4.1 Web-based Parenthetical Translation Extraction . . . . . . . . . . . . . . 31
4.1.1 Parenthetical Translation Extraction Approach . . . . . . . . . . 33
4.1.2 Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
4.1.3 Experiment Design . . . . . . . . . . . . . . . . . . . . . . . . . 44
4.1.4 Evaluation Metrics . . . . . . . . . . . . . . . . . . . . . . . . . 45
4.1.5 Result . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
4.2 Machine Transliteration . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
4.2.1 Preprocessing . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
4.2.2 Many-to-Many Alignment . . . . . . . . . . . . . . . . . . . . . 50
4.2.3 DirecTL+ Training . . . . . . . . . . . . . . . . . . . . . . . . . 51
4.2.4 Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
4.2.5 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
4.3 Transliteration Phonetic Similarity . . . . . . . . . . . . . . . . . . . . . 54
4.3.1 Transliteration Identification Method in Chinese . . . . . . . . . 56
4.3.2 Phonetic similarity . . . . . . . . . . . . . . . . . . . . . . . . . 57
5 Cross-Language Encyclopedia Article Linking 64
5.1 Candidate Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
5.2 Candidate Ranking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66
5.2.1 Title Matching and Title Similarity Feature (Baseline) . . . . . . 67
5.2.2 Bilingual Topic Model Similarity Feature (BTMS) . . . . . . . . 67
5.2.3 Mixed-language Topic Model Similarity Feature (MTMS) . . . . 69
5.2.4 Hypernym Translation Feature (HT) . . . . . . . . . . . . . . . . 70
5.2.5 English Title Occurrence Feature (ETO) . . . . . . . . . . . . . . 72
5.2.6 Infobox Similarity Feature (IS) . . . . . . . . . . . . . . . . . . . 72
5.2.7 Transliteration Phonetic Similarity (PS) . . . . . . . . . . . . . . 74
5.3 NIL Candidate Threshold . . . . . . . . . . . . . . . . . . . . . . . . . . 75
6 Evaluation and Discussion 77
6.1 Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
6.1.1 Evaluation Set . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
6.1.2 Comparison to Other Methods . . . . . . . . . . . . . . . . . . . 78
6.1.3 Evaluation Metrics . . . . . . . . . . . . . . . . . . . . . . . . . 79
6.1.4 Evaluation Results . . . . . . . . . . . . . . . . . . . . . . . . . 80
6.2 Effectiveness of Translation Methods . . . . . . . . . . . . . . . . . . . . 84
6.2.1 Parenthetical Translation . . . . . . . . . . . . . . . . . . . . . . 85
6.2.2 Machine Transliteration . . . . . . . . . . . . . . . . . . . . . . 86
6.3 Comparision Between Bilingual LDA and Mixed-Language LDA . . . . 88
6.4 Effectiveness of the Bilingual LDA (BTMS Feature) . . . . . . . . . . . 89
6.5 Performance Analysis of Each Feature . . . . . . . . . . . . . . . . . . . 93
6.6 Difficult Cases in Baidu Baike . . . . . . . . . . . . . . . . . . . . . . . 97
6.7 False Positive Errors in the Third Dataset . . . . . . . . . . . . . . . . . 98
6.8 Extension to Other Language Pairs (Japanese and Korean). . . . . . . 99
7 Conclusion 102
References 104


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