一個以問答格式文件為基礎的自動問答系統必須能接受使用者以自然語言撰寫問句，並在文件集當中找出相關的文件供使用者參考。截至目前為止大部分的問答技術研究都集中在單一語言的處理之上，換言之，這些研究所處理的使用者問句以及文件集當中的文件皆屬於同一種語言。然而由於商業環境的全球化和網際網路技術的長足進步，不論是個人與組織都必須同時處理各種不同語言的問答格式文件。為了讓使用者能夠更便利地使用自然語言來存取各種語言的問答格式文件，跨語言問答技術的需求與日俱增。基於上述跨語言問答技術的重要性與需求，本研究提出以統計式辭典為基礎的跨語言問答技術，並以單語問答技術以及以機器翻譯為基礎之跨語言問答技術作為效能上的參考與比較基準。實驗結果顯示，本研究所提出之跨語言問答技術能夠達到令人滿意的結果。同時，本研究所提出之以統計式辭典為基礎的跨語言問答技術優於以機器翻譯為基礎之跨語言問答技術。

Question answering from a corpus of question-answer (QA) pairs accepts a user question in a natural language, and retrieves relevant QA pairs in the corpus. Most of existing question answering techniques are monolingual in nature. That is, the language used for expressing a user question is identical to that for the QA pairs in the corpus. However, with the globalization of business environments and advances in Internet technology, more and more online information and knowledge are stored in the question-answer pair format on the Internet or intranet in different languages. To facilitate users’ access to these QA-pair documents using natural language queries in such a multilingual environment, there is a pressing need for the support of cross-lingual question answering (CLQA). In response, this study designs a thesaurus based CLQA technique. We empirically evaluate our proposed CLQA technique, using a monolingual question answering technique and a machine translation based CLQA technique as performance benchmarks. Our empirical evaluation results show that our proposed CLQA technique achieves a satisfactory effectiveness when using that of the monolingual question answering technique as a performance reference. Moreover, our empirical evaluation results suggest our proposed thesaurus based CLQA technique significantly outperforms the benchmark machine translation based CLQA technique.

Chapter 1. Introduction 8
1.1 Research Background 8
1.2 Research Motivation and Objectives 9
1.3 Organization of the Thesis 10
Chapter 2. Literature Review 11
2.1 Monolingual Question Answering Techniques 11
2.2 Cross-Lingual Question Answering (CLQA) Techniques 14
Chapter 3. Design of Cross-Lingual QA-Pair Question Answering (CLQA) Technique 18
3.1 Cross-lingual Thesaurus Construction 19
3.2 Question Representation 22
3.3 Question Type Identification 22
3.4 Term Vector Translation 27
3.5 QA Matching 28
Chapter 4. Empirical Evaluation 33
4.1 Data Collection 33
4.2 Performance Benchmarks 34
4.3 Evaluation Procedure and Criteria 36
4.4 Parameter Tuning Experiments and Results 37
4.4.1 Parameter Tuning for Monolingual Question Answering Technique 37
4.4.2 Parameter Tuning for Machine Translation Based CLQA Technique 42
4.4.3 Parameter Tuning for the Proposed Thesaurus Based CLQA Technique 44
4.5 Comparative Evaluations 47
Chapter 5. Conclusion and Future Research Directions 50
References 53

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