在現實生活中，已標類的資料(labeled data)有時是稀少且珍貴的。在文件分類(text classification)上，我們正面臨此類的問題，我們必須耗費相當的人力去閱讀一篇文章才能正確地將其標類。某些已發展的技術用來克服這樣的問題。然而，我們提出一種方法能夠自動地、持續地、便捷地收集到大量的已標類資料。
一種叫做Really Simple Syndication (RSS) 的結構化文件被用來儲存並傳輸新的文章，而且大多數的RSS都會含有一個討論主題。藉由RSS這樣的特性，我們就能將RSS所大量收集到的文章標注上它們的類別，亦即它們的討論主題。在我們的實驗當中，我們從網路上收集到若干個不同類別的RSS，並且架設一台伺服器持續的向這些RSS收集文章。我們將這些文章儲存到一個資料庫並且記錄它們的類別。在我們的設定下，我們的系統可以輕鬆地在一天內收集到上千篇的文章。再者，我們嘗試著去驗證以這樣的方式所收集到的文章是可靠的。因此，我們使用概念擷取法(concept extraction)擷取每個類別的概念字(concept tokens)並且以平滑式支撐向量機(smooth support vector machines)當作我們的分類器來測試我們收集的資料。此外，我們還利用該分類器去預測我們額外從兩個網站上收集到的文章。這些實驗的結果都是令人滿意的。最後，我們希望這個系統能夠解決在文件分類上已標類資料缺少的問題。

In the real world, the labeled data are sometimes very few and expensive. In text classification, we are facing this kind of problems, it demands a lot of human effort to read over and correctly label an article. Some techniques have been developed to conquer this problem. However, we came up with an approach which collects enormous labeled data automatically, permanently and quickly.

A structured document called Really Simple Syndication (RSS) was created to store and transport new articles. Mostly, an RSS feed will stick with a topical subject. Due to this characteristic of RSS, we can collect articles from RSS and assign the subject as a class label to those collected articles. In our works, we chose a certain amount of RSS feeds with various topics on the Internet, and we build up a web crawler to keep crawling these RSS feeds. We stored those collected articles into a database and recorded their subjects. In our setup, our system can effortlessly collect thousands of labeled articles in one day. Furthermore, we attempt to explain that using this method to collect data is reliable. Therefore, we use concept extraction method to extract the concept tokens and smooth support vector machines as our classification method to test our dataset. Moreover, we use the classifier to predict 2 more extra websites which we collected exclusively. These experiments provide satisfied results. Finally, we expect that our system can be used to solve the problem of lacking labeled data in text classification.

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