現今在網際網路上傳遞的文件成指數倍數的成長，如何能有效正確地幫助使用者找到所需要的文件成為一大議題。將文件歸類至事先指定類別的文件分類技術可以加速文件搜尋的過程並增加搜尋結果的品質。但是文件分類本身亦面臨二大難題，亦即詞彙屬性選擇問題與詞彙語意問題。在詞彙屬性選擇方面，可以將文件先作摘要而縮減文件大小並進一步減低詞彙屬性數量；在詞彙語意問題，目前可以以潛在語意索引技術將詞彙的真實意義藉由於其它詞彙的關連而充分顯現。但是少有研究文獻指出此二種方法結合使用的效果影響。所以本研究的目的就在於提出SBDR，結合文件摘要與潛在語意索引，以解決上述文件分類問題，並進而驗證之。
本研究進行二個實驗來驗證SBDR的績效。實驗一針對文件摘要否對文件分類有幫助，以及用何種詞彙來決定文句的重要性作探討。結果顯示，文件摘要果如預期地能提昇文件分類的績效；此外，同時考量名詞-名詞與名詞-動詞比只考量名詞-名詞來決定文句的重要性較好。實驗二則是比較文件摘要否結合潛在語意索引的績效影響。雖然結果顯示潛在語意索引應用於全文(無摘要)之準確度比應用於摘要文件(SBDR)稍微要佳，但其運算時間亦隨詞彙屬性數量增加而增加。因此SBDR可以應用在以效率為主要考量並且容忍效能少量減損的情況下。此二實驗之結果因此驗證SBDR的實際適用性。

Text categorization to automatically assign documents into the appropriate pre-defined category or categories is essential to facilitating the retrieval of desired documents efficiently and effectively from a huge text depository, e.g., the world-wide web. Most techniques, however, suffer from the feature selection problem and the vocabulary mismatch problem. A few research works have addressed on text categorization via text summarization to reduce the size of documents, and consequently the number of features to consider, while some proposed using latent semantic indexing (LSI) to reveal the true meaning of a term via its association with other terms. Few works, however, have studied the joint effect of text summarization and the semantic dimension reduction technique in the literature. The objective of this research is thus to propose a practical approach, SBDR to deal with the above difficulties in text categorization tasks.
Two experiments are conducted to validate our proposed approach. In the first experiment, the results show that text summarization does improve the performance in categorization. In addition, to construct important sentences, the association terms of both noun-noun and noun-verb pairs should be considered. Results of the second experiment indicate slight better performance with the approach of adopting LSI exclusively (i.e. no summarization) than that with SBDR (i.e. with summarization). Nonetheless, the minor accuracy reduction can be largely compensated for the computational time saved using LSI with text summarized. The feasibility of the SBDR approach is thus justified.

TABLE OF CONTENTS
CHAPTER 1 Introduction 1
1.1 Overview 1
1.2 Objective of the research 2
1.3 Organization of the thesis 3
CHAPTER 2 Literature Review 4
2.1 Information Retrieval 4
2.2 Text Mining 9
2.3 Text Summarization 10
2.4 Text Categorization 11
CHAPTER 3 Proposed Approach 15
3.1 Document Preprocessing 15
3.2 Text Summarization 18
3.4 Dimension Reduction 23
3.5 Categorization 24
CHAPTER 4 Experiments and Results 26
4.1 Experimental Design 26
4.2 Experiment I 30
4.3 Experiment II 35
CHAPTER 5 Conclusion and Future Work 41
5.1 Concluding remarks 41
5.2 Future Work 42
REFERENCE 43

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