臺灣博碩士論文加值系統

網路的發展，有越來越多的文字資訊被產生出來，但是要如何將海量的資料正確地有組織的分類是一件困難的工作，為了要達到這個目的，因此有許多監督式學習演算法被應用在文件分類領域上，而監督式演算法有一些缺點，要做監督式學習需要大量的標記文件來當訓練資料，而要收集標記文件是需要花很多時間和人力，而本論文提出了一個非監督式學習演算法應用在文件分類上，它能改善監督式學習的需要預先收集大量的標記資料這個缺點，利用維基百科、WordNet以及Google搜尋引擎當作外部知識庫，藉由這些外部知識庫能夠準確的計算關鍵字間的語義關係或相關度，並且準確的分類到正確的類別，由初步實驗結果可證明，我們提出的方法有更佳的績效結果。

With swift growth of online text, how to organize text data effectively has become a major issue. Text classification is the task of classifying documents into pre-defined categories. For this, many supervised classification methods have been proposed. But supervised learning methods have some disadvantage. The biggest bottleneck is the requirement of a large amount of training data for better classification performance. While unlabeled documents are simply collected and abundant, labeled documents are difficult to collect because labeling is usually done manually. The task is time-consuming. To overcome those disadvantages and achieve better classification accuracy without labeled data, we propose the combination of three external sources “Wikipedia”, ”WordNet” and ”Google distance” for text classification on unsupervised learning. The result of experiments shows that the combination of Wikipedia with WordNet achieves better performance than the individual methods

圖目錄.... iv
表目錄… v
一、 緒論 1
1.1研究動機 1
1.2 研究目的 1
二、 文獻探討 3
2.1 非監督式學習 3
2.2 相似度測量 3
三、 方法 6
3.1系統架構 6
3.2 預處理 7
3.2.1 命名實體識別 8
3.3 維基百科過濾文章關鍵字 10
3.4 維基百科測量兩字詞關係 11
3.5全面測量特徵選取 13
3.6 相似度測量 14
3.7 搜尋引擎計算兩字詞相關性 16
四、 實驗結果 18
4.1 資料集與實驗設計 18
4.2 績效指標與實驗結果 19
五、結論 24
參考文獻 25
附錄一 各種方法組合之公式列表 27

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