臺灣博碩士論文加值系統

由於網際網路以及資訊技術的發達，資訊檢索(Information retrieval, IR)系統被廣泛應用於日常生活中。資訊檢索系統有助於找出與使用者需求相關之資訊，為現今資訊系統使用者不可或缺之應用，然而其價值往往取決於檢索結果的品質。使用者不僅要求資訊檢索系統要能夠找出其所需要之相關資訊，更希望找出之資訊能夠依照其與使用者需求之相關程度加以排序，以節省其過濾資訊所需耗費之時間。過去許多學者發現相關性回饋(Relevance feedback, RF)資訊相當有助於改善資訊檢索系統的品質；其中，最著名的便是標準的Rocchio相關性回饋演算法(standard Rocchio’s relevance feedback algorithm)。而自動相關性回饋（pseudo relevance feedback）可依查詢結果自動選取相關文件及非相關文件，避免人工提供相關性回饋資訊之負擔與不便，更讓相關性回饋的應用得以在資訊檢索系統上實現。雖然過去所提出的相關性回饋演算法已被證實可以改善資訊檢索品質，然而在相關性回饋資訊的運用上，所有相關文件或非相關文件對於關鍵詞權重所產生的影響均一視同仁，並未考慮到各個文件以及各個關鍵詞彙對查詢之重要程度不一，應給予不同之回饋，方能將相關性資訊做最妥善之應用。有鑑於此，本論文提出一套增強型的自動相關性回饋演算法(Enhanced Pseudo relevance feedback algorithms)，考慮文件及詞彙對查詢的個別重要性，以更進一步改善資訊檢索的品質。從實驗結果可證實，本論文提出之增強型自動相關性回饋演算法優於標準Rocchio相關性回饋演算法。

Owing to the rapid growth and popularization of Internet and information technology, information retrieval systems has become a necessary part of our modern life. Users find valuable information from either digital libraries or the Internet by a few keywords or a nature language sentence. However, the quality of an information retrieval system relies heavily on the accuracy of the information retrieved. The retrieved information should be not only matched the user’s query, but also ranked well according to its relevance to the user’s query. In the literatures, researchers found that Relevance Feedback (RF) information is quite useful for an information retrieval system to improve its accuracy. Among the proposed relevance feedback algorithms, the standard Rocchio’s relevance feedback algorithm is the most well-known and widely employed in information retrieval systems. Furthermore, the idea of pseudo relevance feedback was proposed for the relevance feedback algorithms. It reduces user’s burden by deciding automatically relevant and irrelevant documents according to the ranks of the retrieval results. Although relevance feedback algorithms can be used to improve retrieval performance, they do not discriminate well the degree of importance on either documents or terms. To cope with this problem, an enhanced pseudo relevance feedback algorithm is proposed in this thesis. Experimental results showed that the performance of the proposed algorithm outperforms the standard Rocchio’s relevance feedback algorithm.

Chapter 1 Introduction 1
Chapter 2 Related Works 4
2.1 Models of Information Retrieval 4
2.2 Information Retrieval with Vector Space Model 5
2.3 Query Reformulation 8
2.4 Relevance Feedback 9
2.4.1 Rocchio’s Relevance Feedback Algorithm 10
2.4.2 Ide’s Relevance Feedback Algorithm 11
2.4.3 Standard Rocchio’s Relevance Feedback Algorithm 12
2.4.4 Pseudo Relevance Feedback 13
Chapter 3 The Enhanced Pseudo Relevance Feedback Algorithm 14
3.1 Basic Ideas 14
3.2 The Inverse-Ranked Algorithm 16
3.3 Similarity-Based algorithm 19
3.4 Inverse-Ranked with Frequency ratio algorithm 22
3.5 Similarity-Based with Frequency ratio algorithm 25
Chapter 4 Experiment and Evaluation 28
4.1 Experimental Environments 28
4.1.1 Test Collections 28
4.1.2 Retrieval Effectiveness Measurements 29
4.1.2.1 Precision 29
4.1.2.2 Mean Average Precision (MAP) 29
4.2 Parameter setting 30
4.3 Performance Evaluations with Medlars 32
4.4 Performance Evaluations with OHSUMED 34
4.5 Summary of experimental results 36
Chapter 5 Conclusions and Future Works 39
References 41

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