本論文提出一個利用網路資源自動發現特定類型的具名實體的方法。根據使用者所輸入的少量具名實體(named entity)，在網路上的擷取相關資訊當作種子(seed)，我們訓練出此類實體的共通特性的語言模型，及可以擷取此類實體的表面樣式(surface pattern)， 之後我們根據訓練結果去網路上找出相同類型的具名實體。
在訓練過程中，我們首先利用網路自動收集所需的種子資料、依據語言學及統計學上的知識從種子資料中訓練出關鍵字詞表及其比重值，並且抽取出可清楚表達此類實體附近的字詞，最後將所有字詞的關聯性統計計算出最適合此類實體的擷取樣式(extraction pattern)。在執行階段，利用機率模組中最高頻的兩個名詞及年份從網路擷取相關資料，經過機率模組過濾出應為此類實體的相關文章，最後利用所學習到的表面樣式，擷取新的具名實體。
我們將提出的想法實作成系統雛形，並且將其實驗結果與Google Sets做比較。各項實驗結果證明我們的方法在準確率上與Google Sets不相上下，但是在涵蓋的範圍上則遠勝於Google Sets。本文的確為發掘具相同類型的專名實體提出了一個有效且簡單的方法。

We introduce a method for finding named entities (NEs) with the same category as a given set of seed named entities on the Web. In our approach, passages containing the given seed NEs are retrieved from the Web and subsequently used to construct linguistic model aimed at discovering more new NEs with the same category from the Web.
The method involves generating a key terms table with word classes from Webpage summaries containing the seed NEs and learning surface patterns containing the seed NEs from these passages. At runtime, we use salient key terms and word classes in the model to find the new Web summaries, filter out unlikely passages and extract the new NEs from the remaining passages using surface patterns.
We presented a prototype system, Name Finder, which applies the proposed method to discover additional NEs for a set of given several NEs. We evaluate and compare Name Finder with Google Sets. The experimental results show that our system produces more NEs with an average precision rate comparable with Google Sets. Our methodology cleanly supports automatic knowledge discovery and ontology extension.

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