Collocational influences on the chinese translations of english verbs and their objects in technical documents

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