本論文提出一個利用網路資源，解決詞彙翻譯歧義的自動學習機制。我們的方法包含了訓練階段及測試階段。在訓練階段，首先取得歧義字的常見搭配詞，並且使用網路資決定歧義字在搭配詞的條件下，最可能的翻譯。最後將利用歧義字與這些搭配詞，查詢搜尋引擎，得到不同翻譯的訓練資料。之後再利用這些訓練資料，學習分辨翻譯歧義的決策表（decision list）。在測試階段，對包含歧義字之句子，檢查歧義字鄰近的搭配字，比對決策表，辨別其所歧義字之正確翻譯。
我們隨機選取英國國家語料庫的例句做為測試資料，人工所標記四個崎義的翻譯。最後評估本論文作法以及現有機器翻譯系統的詞彙翻譯正確率。由實驗結果顯示，本文的作法可以在沒有人工標示的訓練資料的條件下，以網路為資料庫，獲得比現有機器翻譯系統更高的詞彙翻譯正確率。

We introduce a new method for automatically disambiguation of word translations by using collocations. In our approach, we learn the the relationships between translation categories and collocations using the information on the Web.
The method consists of a training stage and a runtime stage. During the training stage, the method involves automatically acquisition of collocates of target words from a large corpus, distinguishing of collocations into two or more parts by translations of a given word, and learning a translation decision list based on sentences with the target word and its collocates automatically acquired from the Web. At runtime, the target word in the given sentence is translated according to the decision list model.
We also describe the implementation of a prototype system of the proposed method, experiments, and evaluation. In the experiment, we used four polysemous words to assess the performance of the method compare the results against judgments made by human subjects. Experimental results indicate that the proposed unsupervised method based on the Web as corpus overcomes the knowledge acquisition bottleneck and provides a promising approach for word translation disambiguation.

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