隨著全球網際網路的蓬勃發展，使用者可以輕鬆的透過搜尋引擎，得到全球豐富的資訊。搜尋引擎根據使用者的查詢詞，與已收集的網頁比對關鍵字，最後搜尋引擎依照其制訂的排名方式，將相關網址列出供使用者點選。然而，此情形將會導致搜尋引擎找出大量的文件，而增加使用者的負擔。因此，本篇論文藉由使用者下達的查詢詞所得到的搜尋結果，透過語法結構，使用動詞與名詞的組合，發展使用者目的偵測之機率模型，可以自動地產生使用者背後真正的目的──使用者目的，最後再利用使用者目的對原始查詢詞的搜尋結果進行重新排名，以減低使用者的負擔。

As the Internet grows quickly, users can easily get rich information from the World Wide Web. Search engines use keywords in users’ queries to match the pages they collected. Then these engines sort related URLs and list search results by their ranking method to let user click them. However, a great deal of search results provided by search engines would add users’ load of reading information. This paper uses search results and the pair of verb and noun occurring in search results to identify user goals. We propose a probabilistic model of user goal identification in order to automatically find the needs behind users (user goals), and then re-rank the search results based on user goal identification.

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