網路上資訊如洪流般不斷的產生，一般必須透過搜尋引擎來蒐集特定的資訊。然而若欲瞭解所蒐集資訊涵蓋的概念，將所有的資訊都瀏覽的方法既費時又不一定得到完整的概念。取而代之的應是使用自動概念萃取的方法來輔助使用者全盤瞭解的所蒐集資訊的涵義。
在本研究中，我們提出一套方法從收集的文件中萃取出概念並偵測隨著時間概念的演進變化。本方法分為兩階段，第一階段藉由潛在語意索引將段落群聚起來並且找出關鍵的字詞，以段落摘要和相關的字詞來充分表達概念，讓使用者可以容易地了解資訊概念的涵義；第二階段為演進式的概念結構修改來代表概念的演進，當新的資訊不斷的加入後，概念可能因此而結合、分裂，甚至產生新的概念。
本研究提出三個實驗來驗證所提出之方法，前兩個實驗的結果都和既定答案有相當高的精確度和回覆率，最後一個實驗是以實際個案(南亞大海嘯)來說明所提方法可以萃取出海嘯報導的整體概念與其演進。這些實驗因此驗證本研究方法的適用性。

To manage the information flood in the Internet, we usually navigate specific information using the provided search engines. Search engines are convenient but with limited functions. For example, it is impractical and impossible to browse through the entire collected information for us to gain an overall picture about what the navigated information stands for. To do so, we need an appropriate approach to automatically extracting concepts from the navigated information to assist users to easily and quickly gain the primary understanding toward a topic that interests users.
In this research, we propose an approach to extracting concepts from the navigated web information and detecting the concept changes over time. It basically includes two stages. In the first stage, information is decomposed into paragraphs and they are clustered with key terms identified through the aid of latent semantic indexing method. Concepts are represented in the form of paragraph summary and associated key terms, which allows the user to easily comprehend what they describe. The second stage is to adaptively modify the concept structure to detect concept changes. With new information added, the concepts could be merging, splitting, or even emerging with time.
Three experiments are conducted in this research to verify the proposed approach. Results of the first and second experiments show both high recall and high precision that matches the predefined concept categories. The last one is an illustrated real case application on the tsunami event. It shows that we can easily grasp different concepts of the tsunami reports and realize their changes by using our approach. The feasibility of employing our approach is thus justified.

CHAPTER 1 Introduction 1
1.1 Overview 1
1.2 Objective of the research 2
1.3 Organization of the Thesis 2
CHAPTER 2 Literature Review 4
2.1 Information retrieval 4
2.1.1 Probabilistic model 4
2.1.2 Vector space model 5
2.1.3 Latent semantic indexing 5
2.2 Text mining 7
2.2.1 Document clustering 8
2.2.2 Text summarization 8
2.2.3 Concept Extraction 9
2.3 Cluster analysis 11
2.3.1 Partitioning clustering 11
2.3.2 Hierarchical clustering 12
2.3.3 Incremental clustering 13
CHAPTER 3 Proposed Approach 15
3.1 Concept Extraction Stage 16
3.1.1 Information Preprocessing 16
(1) Part-of-speech tagging 17
(2) Noun stemming 17
(3) Term Frequency Calculating 18
3.1.2 Similarity Calculating 19
3.1.3 Paragraph Clustering 21
3.1.4 Concept Extracting 22
3.2 Concept Change Analysis Stage 24
3.2.1 New Paragraphs Joining 24
3.2.2 Intra Cluster Reorganizing 26
3.2.3 Detecting concept changes 27
(1) Slightly Changing 28
(2) Merging 28
(3) Splitting 30
3.2.4 Structure Re-Building 30
CHAPTER 4 Experiments and Results 32
4.1 Experiment I 32
4.2 Experiment II 38
4.3 An Illustrated Application 41
CHAPTER 5 Conclusions 46
REFERENCES 48
Appendix I The Complete Summaries Under Each Concept Category 52
Appendix II The Most Representative Paragraphs of Tsunami News 56

賴志民,網際網路上資訊涵意探究與資訊變化追蹤之研究,中山大學資訊管理研究所碩士論文,民91
張家豪,網際網路搜尋資訊之涵意探究及其變化偵測,中山大學資訊管理研究所碩士論文,民93
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