臺灣博碩士論文加值系統

自動查詢擴展技術在許多資訊檢索相關研究中，已被證實可以有效增進檢索效能，此技術主要解決檢索過程中字彙不匹配 (Word mismatch)的問題，即使用者所提供的查詢字彙與文件使用字彙間差異所導致的檢索效能不佳問題。

本研究延伸機率模型之非隨機性概念，提出一查詢擴展演算法，並使用初始檢索排名前幾篇之文件為擴展關鍵字來源，利用Rocchio架構重新衡量候選關鍵字權重與選取擴展關鍵字，再加以進行查詢擴展。

經使用單一主題領域之Cranfield與多主題領域之npl測試資料集進行實驗與分析後，結果顯示本研究提出之方法可有效提升檢索效能。除此之外，本研究也針對影響查詢擴展效能的相關參數，進行詳細的實驗與分析，這些參數包括虛擬相關文件集合篇數、擴展關鍵字數目與Rocchio架構下之參數。

Automatic query expansion addresses the problem of word mismatching that the words provided by the users in the query are not consistent with the words used by the authors. The problem of word mismatching can result in poor retrieval effectiveness. Many techniques of automatic query expansion have been developed and proved to improve retrieval effectiveness.

We apply the concept of the non-randomness of probabilistic model to conceive a method for automatic query expansion. Top-ranked documents that are retrieved in the initial retrieval are used as the source of expansion terms. The candidate expansion terms are re-weighted and selected within Rocchio framework.

Experimenting results show that our approach can improve the effectiveness of retrieving significantly. The experiments have the parameters that can influence the performance of automatic query expansion considered and analyzed, including number of selected documents, number of expansion terms and parameters in the Rocchio framework.

第一章 緒論 1
1.1 研究背景與動機 1
1.2 研究目的 2
1.3 研究範圍與限制 2
1.4 研究流程 3
1.5 論文架構 5
第二章 文獻探討 6
2.1 自動查詢擴展 6
2.2 自動查詢擴展基礎架構 9
2.3關鍵字重新衡量權重與查詢重組 13
2.4 非隨機分佈理論 15
第三章 系統設計 17
3.1 研究構想 17
3.2 延伸非隨機模型查詢擴展演算法 18
3.3 功能性系統架構 23
第四章 實驗分析 28
4.1 測試資料集、基底檢索演算法與實驗平台 28
4.2 ENR查詢擴展演算法效能 31
4.3不同基底演算法對查詢擴展之影響 34
4.4 查詢效能增進程度分析 36
4.5 擴展後無檢索結果之查詢分析 38
4.6 參數組合分析 40
4.7 個別查詢參數最佳化分析 45
4.8 不同查詢擴展演算法之跨測試資料集分析 46
第五章 結論 54
5.1 研究結論與貢獻 54
5.2 未來研究方向 56
參考文獻 58

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