本研究使用英漢平行語料庫，試圖從中找尋英文與中文之間的翻譯情形，我們將英文及中文的動名詞組合 (V-N-collocation) 作為觀察對象。本研究各別分析英漢專利平行文句語料庫及科學人雜誌英漢對照電子書兩套語料庫，將中英文互為翻譯的文件視為一體，觀察英文及中文語言其中的特定結構及共現性 (collocation) ，建構由真實世界的語料所反應的語言翻譯模型。 　　我們使用技術名詞表將平行語料庫進行技術名詞斷詞，再將句子進行結構剖析得到關係樹 (dependency tree) ，並利用關係樹結構及近義詞典取得英漢動名詞組合。本研究運用英漢動名詞組合建立英文動詞與名詞的翻譯模型，我們的系統可以根據不同的模型推薦翻譯，並比較這些翻譯模型的成效；最後也加入中文語言使用者翻譯英文動詞的實驗與本研究的翻譯模型效果作比較，結果顯示本研究的翻譯模型比起受試者，可以有較好的推薦效果。 / In our investigation, we are interested in English Verb-Noun collocation (V-N collocation) and the corresponding usage in Chinese. To discover English-Chinese V-N collocation, a rich corpus is needed; therefore, we obtained one million English-Chinese parallel patent sentence pairs and seven years of bilingual Scientific American as two corpora to analyze. We trained translation models to find the usage of V-N collocations in English and Chinese. Given English V-N collocation and corresponding Chinese information, our system can recommend the proper translations of the English verb or object in collocation according to the translation models. We experimented ten formulas to train our models using two corpora, and observed similar trends in the analyses. Preliminary comparisons of the translation quality of human subjects and our system indicated that our system could offer better recommendations for the translation tasks.

英文介系詞在句子裡所扮演的角色通常是用來使介系詞片語更精確地補述上下文，英文的母語使用者可以很直覺地使用。然而電腦不瞭解語義，因此不容易判斷介系詞修飾對象；非英文母語使用者則不容易直覺地使用正確的介系詞。所以本研究將專注於介系詞片語定位與介系詞推薦的議題。 在本研究將這二個介系詞議題抽象化為一個決策問題，並提出一個一般化的解決方法。這二個問題共通的部分在於動詞片語，一個簡單的動詞片語含有最重要的四個中心詞（headword）：動詞、名詞一、介系詞和名詞二。由這四個中心詞做為出發點，透過WordNet做階層式的選擇，在大量的案例中尋找語義上共通的部分，再利用機器學習的方法建構一般化的模型。此外，針對介系詞片語定的問題，我們挑選較具挑戰性介系詞做實驗。 藉由使用真實生活語料，我們的方法處理介系詞片語定位的問題，比同樣考慮四個中心詞的最大熵值法（Max Entropy）好；但與考慮上下文的Stanford剖析器差不多。而在介系詞推薦的問題裡，較難有全面比較的對象，但我們的方法精準度可達到53.14%。 本研究發現，高層次的語義可以使分類器有不錯的分類效果，而透過階層式的選擇語義能使分類效果更佳。這顯示我們確實可以透過語義歸納一套準則，用於這二個介系詞的議題。相信成果在未來會對機器翻譯與文本校對的相關研究有所價值。 / This thesis focuses on problems of attachment of prepositional phrases (PPs) and problems of prepositional suggestions. Determining the correct PP attachment is not easy for computers. Using correct prepositions is not easy for learners of English as a second language. I transform the problems of PPs attachment and prepositional suggestion into an abstract model, and apply the same computational procedures to solve these two problems. The common model features four headwords, i.e., the verb, the first noun, the preposition, and the second noun in the prepositional phrases. My methods consider the semantic features of the headwords in WordNet to train classification models, and apply the learned models for tackling the attachment and suggestion problems. This exploration of PP attachment problems is special in that only those PPs that are almost equally possible to attach to the verb and the first noun were used in the study. The proposed models consider only four headwords to achieve satisfactory performances. In experiments for PP attachment, my methods outperformed a Maximum Entropy classifier which also considered four headwords. The performances of my methods and of the Stanford parsers were similar, while the Stanford parsers had access to the complete sentences to judge the attachments. In experiments for prepositional suggestions, my methods found the correct prepositions 53.14% of the time, which is not as good as the best performing system today. This study reconfirms that semantic information is instrument for both PP attachment and prepositional suggestions. High level semantic information helped to offer good performances, and hierarchical semantic synsets helped to improve the observed results. I believe that the reported results are valuable for future studies of PP attachment and prepositional suggestions, which are key components for machine translation and text proofreading.

本論文應用以範例為基礎的機器翻譯技術，應用英漢雙語對應的結構輔助英漢單句語料的翻譯。翻譯範例是運用一種特殊的結構，此結構包含來源句的剖析樹、目標句的字串、以及目標句和來源句詞彙對應關係。將翻譯範例建立資料庫，以提供來源句作詞序交換的依據，接著透過字典翻譯，以及利用統計式中英詞彙對列和語言模型來選詞，最後填補缺少的量詞，產生建議的翻譯。我們是以2003年國際數學與科學教育成就趨勢調查測驗詴題為主要翻譯的對象，以期提升翻譯的一致性和效率。以NIST 和BLEU 的評比方式，來評估和比較Google Translate 和Yahoo!線上翻譯系統及本系統所達成的翻譯品質。我們的系統經過詞序調動以及填補量詞後，翻譯品質比我們前一代系統要佳，但整體效果沒有比Google Translate 和Yahoo!線上翻譯的品質要佳。 / This paper presents an example-based machine translation based on bilingual structured string tree correspondence (BSSTC). The BSSTC structure includes a parse tree in source language, a string in target language and the correspondence between the source language tree and the target language string. / We designed an English to Chinese computer assisted translation system for Trends in International Mathematics and Science Study (TIMSS), through the BSSTC structure reordering, directory translation, choosing translation statistics model and measure word generation. / We evaluated our system by the BLEU and NIST score and compared with Google Translate and Yahoo! Translate. By reordering selected word sequences and inserting measure words in the default translations, the current system achieved a higher quality of default translations than the previous implementation of our research group, but the overall effects still lag behind that achieved by Google and Yahoo!.

綜觀現今全球化的趨勢，世界各國皆進行跨語言的專利文書翻譯工作。在專利文書翻譯及跨語言檢索方面，蒐集大量且正確的專利文書平行語料能夠協助相關研究的進行。利用人工進行平行語料文句的對列工作相當費時，因此，本研究利用斷句、斷詞及英文詞幹還原等前處理技術，搭配中英技術名詞對應表，透過統計詞頻調整對應詞組的權重，並以句子間的餘弦相似度作為輔助，計算中英文句子間的相似度，最後利用動態規劃演算法挑選最佳的對列組合，發展出一套中英文句對列的系統。以精確率及召回率評比對列成效，並將對列後產生的句對作為輔助式機器翻譯系統詞序調動的訓練語料，以2003年國際數學語科學教育成就趨勢調查測驗試題作為翻譯對象，採用BLEU及NIST的評比方式進行評估。實驗結果顯示本系統不僅在1：1對列模式的精確率達到0.995，且利用門檻值篩選出的大量中英文句對，確實能夠提升輔助式機器翻譯系統的翻譯品質。 / The importance of cross-language translation of patent documents has grown substantially as a result of globalization. Accurately aligned parallel corpora help researchers conduct their research projects that depend on bilingual data to develop techniques such as computer-aided translation and cross-language information retrieval. It takes time to collect parallel data manually; therefore, an English-Chinese sentence alignment system was built that will automatically complete this process. A variety of preprocessing techniques for natural language processing were used, such as the stemming of the English words, to build this system. Two parts of scores were considered to align sentences. The first part considered the number and weight of aligned word pairs in the Chinese and English sentences. The second part came from a special way to compute the cosine value of the Chinese and English sentence pairs. Precision and recall rates were used to evaluate the quality of the aligned results and the 1:1 alignment achieved 0.995 precision. In addition, the aligned sentences were used as training data in a machine translation for the TIMSS test items, experimental results show that the aligned sentences are helpful for the translation system.

由國際教育學習成就調查委員會統一命題之國際數學與科學教育成就趨勢調查測驗，為便於台灣中小學生施測與理解，英文原文試題內容需要經過許多人工討論及翻譯時間。為了增進翻譯內容一致性及其效率，我們設計一套符合測驗試題的輔助翻譯系統，將不同格式的試題文件，經執行語法分析式的片語擷取和字典查詢，透過使用者介面，選擇合適的片語詞彙翻譯選項和詞序調整，以及提供目前常用之線上翻譯服務、回顧翻譯類似句、以及加減詞彙等功能。為了能提昇翻譯詞彙的選擇正確性，我們記錄翻譯者選詞動作，讓翻譯者能回顧過去曾處理過的翻譯類似句，並且按照系統提供之選詞頻率資訊、科學領域的期刊語料之詞頻統計，以及利用統計式中英詞彙對列和語言模型，更改選詞的優先順序。我們嘗試以過去試題為實驗對象，按年級及學科區分6大試題類別，搭配4種選詞策略，透過BLEU及NIST之翻譯評估指標比較線上翻譯系統和本系統，實驗結果顯示在各實驗組的評估上均有優於線上翻譯系統的效果。 / Test items used in the Trends in International Mathematics and Science Study (TIMSS) are designed by The International Association for the Evaluation of Education Achievement, for facilitating education scientists to measure students’ competence in science and mathematics. Translating the English items into Chinese items demands a lot of work. Therefore, we would like to offer a computer-aided translation environment to improve the consistency and efficiency of the translation process. Through the user interface, translators could input different document format of test items, use phrase analysis and dictionary to find different phrase translations, and adjust word orders. Users of our system may obtain translations from on-line translations provided by Google and Yahoo, can look for previously translated items that contain specific word patterns, and so on. For selecting appropriate Chinese translations for English words, we considered users’ past selection, word frequencies in relevant corpora, and other language-related information in parallel corpora. We employed test items used in TIMSS 1999 and TIMSS 2003 to evaluate the effectiveness of our translation environment. Translations recommended by our system were compared with actual Chinese translations of the test data, and the similarity was measured with the BLEU and NIST metrics. Experimental results indicate that our system performed better or similarly with Google and Yahoo on-line translation systems.