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研究生:杜協昌
研究生(外文):Hsieh-Chang Tu
論文名稱:互動式網際網路檢索:模型、度量及實驗
論文名稱(外文):Interactive Web IR: Focalization Model, Effectiveness Measures, and Experiments
指導教授:項潔項潔引用關係
指導教授(外文):Jieh Hsiang
學位類別:博士
校院名稱:國立臺灣大學
系所名稱:資訊工程學研究所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:1999
畢業學年度:87
語文別:英文
論文頁數:155
中文關鍵詞:互動式檢索網際網路聚焦檢索模型有效性度量聚光燈式提示多維度導覽
外文關鍵詞:Interactive IRWorld Wide WebFocalization ModelEffectiveness MeasuresSpotlight CuesMulti-dimensional Navigation
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由於 World Wide Web(網際網路)上資料量龐大,有必要研究 interactive IR(互動式資訊檢索)來增進檢索效率。本論文的目的在於構建一個模型,來討論當前許多 interactive IR services 在檢索中所扮演的角色。其次,要評估檢索系統的效率,我們必須有合適的 effectiveness measures (有效性度量)。本論文討論傳統的一些有效性度量,並試著提出比較切於實際的方法,來評估檢索效率。最後,我們也討論如何利用簡單的文件屬性,來增進 reranking(檢索結果重新排序)的效率。
本論文有三個主要子題,分別是
1. 一個概念上相當簡單的 interactive IR 操作型模型:嘗試利用數種型態的 operators,定義 interactive Web IR 的行為﹔也就是說,我們可以利用不同的 operator sequences 來規範出不同的檢索行為。我們也提出一個 spotlight cues(聚光燈式提示)的觀念,來表示系統建議使用者可能感興趣的文件。我們並由此觀念伸衍出對 browsing(瀏覽)行為有助益的 "multi-dimensional navigation" (多維度檢索導覽)應用。
2. 我們提出五項理由,來說明為何 recall(回收率,是一個在傳統 IR 中,被廣為接受的有效性度量)在 Web IR 上,是不實際的。我們也提出兩個比較實際的度量(稱為 "DCV-precision plot" 和 "relnum-precision plot"),並且討論它們的一些特性。
3. 我們做了實驗,利用相當簡單的 Web 文件屬性(例如 keywords、anchors、以及「相關 terms」的數量),對 search engines 所傳回的結果做 reranking。我們也討論如何利用這些簡單屬性來增進檢索效率。
另外,本論文在「背景資訊」的複習章節中,討論了許多 probabilistic IR 的問題,也列出許多網際網路上現有的互動式技術。我們在論文最後給了幾項結論,並試著提出一些未來研究的方向。
封面
1 Introduction
1.1 Research Methods
1.2 Contribution
1.3 Limitation
1.4 Thesis Organization
2 Background
2.1 Text Retrieval Models
2.2 Probabilistic IR Theries
2.3 Classical Interactive IR
2.4 Web Search Engines
2.5 Agents that Assist Web IR
2.6 Traditional Effectiveness Measures
3 Related Work & Literature Review
3.1 Text Retrieval Models and Techniques
3.2 Classical Interactive IR
3.3 Web Information Retrieval
3.4 User Studies and IR Evaluation
4 Focalization: A Model on Interactive Web IR
4.1 Characteristics of Web IR
4.2 Interactive IR: A Sequence of Focus Changes
4.3 The Formal Focalization Model
4.4 Application: The BibTex Agent
5 Effectiveness Measures for Evaluationg Web IR
5.1 Advantages of Relevance-Based Measures
5.2 Arguments againt Recall-Based Measures
5.3 DCV-Precision and Relnum-Precision Plots
6 Experiments and Findings
6.1 Experiment Steup
6.2 Statistics about Document Assessments
6.3 The Effect of Keywords and Anchors
6.4 The Effect of Matched Relevant Terms
6.5 The Effect Terms Extracted from Top-100 Documents
6.6 A Summary on the Findings
7 Conclusion and Future Work
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