在本論文中，我們提出抽取英文多字詞語(multiword expression)之中文對應的新方法。主要的概念是延續Brown等人 (1991)及Gale等人 (1991)對單字翻譯與對應的研究構想。Brown觀察到字義會隨上下文而變化，因此可由上下文更精確決定單字的翻譯；而Gale則利用單字構詞變化型態，由較高頻的雙語詞彙對應，抽取出其他頻率較低的對應。受到這兩項研究的啟發，我們使用現存的詞彙對應工具，估算出詞彙的初始詞彙翻譯機率(lexical translation probability)，再加上針對上下文的考量與詞素(morpheme)修改詞彙翻譯機率。將多字詞語依照修改後的語彙翻譯機率進行逐字對應後，便能輕易地擷取其翻譯。
我們實際製作了程式，以香港平行新聞語料庫的74萬句平行語料為基礎，嘗試擷取語料中的英文多字詞語與其中文對應。我們從新聞語料庫及WordNet語料庫中隨機抽取動詞片語進行測試。兩組實驗都顯示，與目前最佳詞彙對應工具比較，無論是在嚴格評估或寛鬆評估的情況下，提出的方法皆有效地提高了對應的精確率。實驗結果顯示，我們的作法能彌補逐字翻譯的不足，且能克服單字對應工具高精確率但低召回率的問題，建立了更可靠也更有彈性的翻譯記憶體，將可以改進詞彙對應與機器翻譯的效果。

In this paper, we introduce a method to align an English multiword expression (MWE) with Chinese translation equivalent (TE) in a given bilingual parallel corpus. In our approach, we make use of an existing word alignment tool which provides information about word alignment results to estimate context-independent lexical translation probability (LTP). However, such estimates for lexical translation probability often have unsatisfactory precision and recall rates due to the inherent limitation of word alignment. Consider context and morphological information may ease the problem. More specifically, words with related meanings usually have some characters in common. Therefore, we build on word-alignment results to estimate context-sensitive LTP at the morphological level for further alignment.
At runtime, we align each word in an English MWE individually with Chinese words in the target sentence according to this new version of LTP, and combine the alignments into the final TE.
We implement the method on verbal MWEs randomly selected from WordNet and several MWEs manually examined from corpus. The evaluation of the experimental results shows our procedure outperforms the underlying word alignment tool. Our methodology helps to establish more precise translation memory, which may improve the performance of machine translation systems.

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