在本論文中，我們提出一個藉由機器翻譯系統，自動對應網路上雙語網頁的方法。我們先利用程式在網路上自動收集擷取大量的雙語網頁，然後利用機器翻譯系統翻譯原文網頁，接著在原文與譯文的網頁中，擷取互為翻譯的中文與英文詞彙，並且記錄文字的位置，統計互為翻譯詞彙的位置關係。之後，我們計算機器翻譯與真實翻譯的相似性，也就是相同字詞重疊的比例，來估計原文網頁與與譯文網頁間的互為翻譯的可能性，比對之前我們先以所提出的位置關係模組，來過濾掉字詞相同，但位置關係不符合常態的對應。位置關係結合相似性比對可以有效率的過濾不正確的對應，進而可以提升搜尋網頁對應的準確率。
我們也將敘述如何將提出的做法實作成程式，以美國之音（Voice of America）的中英文雙語新聞網頁為實驗語料，並使用三種機器翻譯系統來測試。實驗結果證明我們的方法表現得令人滿意，所對應到的中英文雙語網頁大約有99%的精確率，及99%的召回率。

We introduce a method which automatically aligns bilingual pages on the Web by using a machine translation system. In our approach, a source page is translated using a MT system and aligned with most similar candidate pages. The method involves collecting bilingual pages on the Web, translating each source page, aligning and evaluating each source page with the possible candidate pages based on some similarity measures. The similarity measure is based on a simple approach of counting the ratio of shared characters between a machine translated page and an actual target pages. In order to achieve optimal performance, shared characters are checked for their positions in both pages. These position pairs deviated from the norm are excluded from consideration in calculating page to page similarity.
We describe an implementation of the method by using three MT systems to align online English and Chinese news pages at Voice of America (VOA) website. Experimental results indicate that English and Chinese Web news pages can be aligned with the average precision rate of 99%. The results indicate that the method can be applied to collect online parallel corpora easily and efficiently, therefore, meeting the increasing demand for parallel corpora to be used as a linguistic resource in various natural language processing tasks.

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