臺灣博碩士論文加值系統

在資訊擷取系統中，查詢索引詞扮演著一個重要的角色，其可以影響文件擷取系統的效能。文件擷取系統可以藉由查詢索引詞擴展的技術及查詢索引詞權重調整的技術以增進系統效能。在本論文中，我們提出兩個方法分別作查詢索引詞擴展及作查詢索引詞權重調整以作文件擷取。第一個方法是根據相關索引詞的重要程度去挑選出新的查詢索引詞，並以模糊規則推論出新查詢索引詞的權重以作文件擷取；第二個方法是根據類神經網路將查詢索引詞的權重調整至最佳化以作文件擷取。本論文所提的方法能增進資訊擷取系統的效能以作文件擷取。

In document retrieval systems, query terms play an important role which can affect the performance of document retrieval systems. The performance of document retrieval systems can be improved by using query terms expansion techniques and query terms reweighting techniques. In this thesis, we present two new methods for query terms expansion and query terms rewieghting. The first method chooses additional query terms for query expansion according to the degrees of importance of relevant terms and use fuzzy rules to infer their weights for document retrieval. The second method adjusts the weights of query terms to be optimal using neural networks for document retrieval. The proposed methods increase the performance of information retrieval systems for dealing with document retrieval.

Abstract in Chinese ………………………………………………………………… i
Abstract in English ………………………………………………………………… ii
Acknowledgements ………………………………………………………………… iii
Contents …………………………………………………………………………….. iv
List of Figures and Tables ..………………………………………………………… vi
Chapter 1 Introduction ………………………………………………………….. 1
1.1 Motivation …………………...……………………………………….. 1
1.2 Related Literature ……...…………………………………………….. 2
1.3 Organization of This Thesis ………………………………………….. 3
Chapter 2 Basic Concepts of Fuzzy Sets and Backpropagation Neural Networks………………………………….………………………………

…..…...……………….
4
2.1 Basic Concepts of Fuzzy Sets ……………………………………………………… 4
2.2 The Representation of Fuzzy Sets …………………………….……………… 6
2.3 Basic Concepts of Backpropagation Neural Networks ...…………………… 11
2.3.1 The Gradient Steepest Descent Method …...…………………………………………………………... 11
2.3.2 The Least Mean Square Algorithm (The Delta Rule) …...…………………………………………………………... 12
2.3.3 Activation Functions (Output Functions) …...…………………………………………………………... 13
2.4 Summary ……………………………………………………………... 17
Chapter 3 A Review of the Existing Query Expansion Method ………………………………………………………….. 18
3.1 The Vector Space Model …..……………………………………………… 18
3.2 Query Reformulation and User Relevance Feedback Techniques …......…………………… 19
3.3 A Review of Chang-Chen- Liau’s Method [4] for Query Expansion Based on Fuzzy Rules…………………………………………………
20
3.4 Summary ……………………………………………………………... 26
Chapter 4 A New Method for Query Expansion Based on User Relevance Feedback Techniques…………………………………………………
27
4.1 The Degree of Importance of a Relevant Term …………………………………….. 27
4.2 Use Fuzzy Rules to Infer the Weight of Each Additional Query Term.. 30
4.3 The Proposed Query Expansion Method Based on User Relevance Feedback Techniques………………………………………………….
…………………………………………………………..
35
4.4 Experimental Results ………………………………………………… 36
4.5 Summary ……………………………………………………………... 42
Chapter 5 A New Method for Query Reweighting Based on Backpropagation Neural Networks………………………………………………………
43
5.1 The Framework of the Proposed Query Reweighting Method ….………...………………………... 43
5.2 The Proposed Query Reweighting Method ….………...………………………... 44
5.3 The Proposed Query Reweighting Algorithm for Document Retreival Based on Backpropagation Neural Networks…………...…………….
…………………………………………………………..
47
5.4 Experimental Results …………………….…………………………... 49
5.5 Summary ……………………………………………………………... 57
Chapter 6 Conclusions …………………………………………………………… 58
6.1 The Contributions of This Thesis …………………………………………………. 58
6.2 Future Research ……………………………………………………… 59
References ……………………………………………………………...…………… 60

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