應用相關回饋之語詞關係於查詢擴展之文件重排序方法

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：98

、訪客IP：3.22.240.165

姓名

蔡家宜(Jia-Yi Tsai) 查詢紙本館藏

畢業系所

資訊管理學系

論文名稱

應用相關回饋之語詞關係於查詢擴展之文件重排序方法
(The application of terms′ relation in relevance feedback for query expansion to the document re-ranking)

相關論文

★ 信用卡盜刷防治簡訊規則製作之決策支援系統	★ 不同檢索策略之效果比較
★ 知識分享過程之影響因子探討	★ 兼具分享功能之檢索代理人系統建構與評估
★ 犯罪青少年電腦態度與學習自我效能之研究	★ 使用AHP分析法在軟體度量議題之研究
★ 優化入侵規則庫	★ 商務資訊擷取效率與品質促進之研究
★ 以分析層級程序法衡量銀行業導入企業應用整合系統(EAI)之關鍵因素	★ 應用基因演算法於叢集電腦機房強迫對流裝置佈局最佳近似解之研究
★ The Development of a CASE Tool with Knowledge Management Functions	★ 以PAT tree 為基礎發展之快速搜尋索引樹
★ 以複合名詞為基礎之文件概念建立方式	★ 利用使用者興趣檔探討形容詞所處位置對評論分類的重要性
★ 透過半結構資訊及使用者回饋資訊以協助使用者過濾網頁文件搜尋結果	★ 利用feature-opinion pair建立向量空間模型以進行使用者評論分類之研究

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

至系統瀏覽論文 ( 永不開放)

摘要(中)

在資訊科技蓬勃發展的時代，資訊檢索 (Information Retrieval)系統對人們而言是獲取資訊的重要管道之一。如何使資訊檢索系統更貼近使用者的資訊需求，一直以來都是被高度關注的研究議題。在相關回饋(Relevance Feedback)領域中，以Rocchio演算法最為廣泛使用，此演算法透過字詞出現的頻率來分析出相關回饋結果中的重要字詞，作為查詢擴展(Query Expansion)的字詞來源，其實作結果具有一定水平的效能且經常被應用於各種檢索中。不過，Rocchio僅以字詞的出現頻率為相關文件的重要字詞依據，未考慮到字詞間是否有其他特性可以運用。本研究運用相關回饋的結果做語詞關係分析，以找出適合作為查詢擴展的字詞，並透過負相關回饋之分析結果修正正相關回饋之分析結果，藉此運用字詞間不同的特性，改善只考慮字詞頻率而忽略其他語詞關係的方法，進而提升文件檢索之準確率。透過實驗證明本研究所提出之方法相較於其他方法，有較佳的檢索效果。

摘要(英)

With the quick development of the information technology, information retrieval system is one of the critical way for people to get information. The research of how the result of information retrieval system can get closer to users’ information need has always been highly concerned. Rocchio’s query expansion algorithm has been widely utilized in relevance feedback. It analyzed the importance of terms residing in relevance feedback by term frequency, and be the source of query expansion terms. The algorithm has been applied in different retrieval due to its simple and effectiveness. However, Rocchio algorithm only focuses on term frequency and ignores other terms’ relation that may be useful. Therefore, in this research, we aimed to develop a method applies terms’ relation in relevance feedback, including synonymy and co-occurrence relation, to analyze terms which are suitable for query expansion. Then, revising relevance feedback query result by non-relevance result to do document re-ranking. Through applying different relation between terms in relevance and non-relevance documents can improve the method only focuses on term frequency and ignores others. The results of experiments verify that the proposed method is effectiveness in document retrieval.

關鍵字(中)

★ 資訊檢索
★ 相關回饋
★ 查詢擴展

關鍵字(英)

論文目次

中文摘要 i
英文摘要 ii
誌謝 iii
目錄 iv
圖目錄 vi
表目錄 viii
一、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 論文架構 3
二、文獻探討 4
2-1 向量空間模型(Vector Space Model) 4
2-2 相關回饋 (Relevance Feedback) 5
2-2-1 相關回饋背景與介紹 5
2-2-2 Rocchio演算法 7
2-2-3 相關回饋之相關研究 8
2-2-4 負相關回饋之相關研究 8
2-3 查詢擴展 (Query Expansion) 9
2-4 WordNet 10
三、研究方法 14
3-1 系統架構 14
3-2 方法設計 15
四、實驗設計 21
4-1 實驗資料 21
4-2 實驗評估指標 24
4-3 實驗流程設計 26
4-3-1 實驗一 27
4-3-2 實驗二 27
4-3-3 實驗三 28
4-4 實驗結果 28
4-4-1 實驗一結果 29
4-4-2 實驗二結果 32
4-4-3 實驗三結果 35
4-5 實驗結果討論 38
五、結論 39
5-1 結論與貢獻 39
5-2 未來研究方向 40
參考文獻 41

參考文獻

〔1〕Furnas, G. W., Landauer, T. K., Gomez, L. M., & Dumais, S. T. (1987). The vocabulary problem in human-system communication. Communications of the ACM, 30(11), 964-971.
〔2〕Salton, G. & McGill, M. J. (1983). Introduction to modern information retrieval.
〔3〕Liddy, E. D. (1998). Enhanced text retrieval using natural language processing. Bulletin of the American Society for Information Science and Technology, 24(4), 14-16.
〔4〕Ruthven, I., & Lalmas, M. (2003). A survey on the use of relevance feedback for information access systems. The Knowledge Engineering Review, 18(02), 95-145.
〔5〕圖書館學與資訊科學大辭典(2012)。向量空間模型。Retrieved 03/20, 2016, from http://terms.naer.edu.tw/detail/1678992/
〔6〕Carpineto, C., Romano, G., & Giannini, V. (2002). Improving retrieval feedback with multiple term-ranking function combination. ACM Transactions on Information Systems (TOIS), 20(3), 259-290.
〔7〕Salton, G. (1971). The SMART retrieval system—experiments in automatic document processing.
〔8〕Kelly, D., & Belkin, N. J. (2001, September). Reading time, scrolling and interaction: exploring implicit sources of user preferences for relevance feedback. In Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval (pp. 408-409). ACM.
〔9〕Kelly, D., & Teevan, J. (2003, September). Implicit feedback for inferring user preference: a bibliography. In ACM SIGIR Forum (Vol. 37, No. 2, pp. 18-28). ACM.
〔10〕Rocchio, J. J. (1971). Relevance feedback in information retrieval.
〔11〕Manning, C. D., Raghavan, P., & Schütze, H. (2008). Introduction to information retrieval (Vol. 1, No. 1, p. 496). Cambridge: Cambridge university press.
〔12〕Dillon, M., & Desper, J. (1980). The use of automatic relevance feedback in Boolean retrieval systems. Journal of Documentation, 36(3), 197-208.
〔13〕Robertson, S. E., van Rijsbergen, C. J., & Porter, M. F. (1980, June). Probabilistic models of indexing and searching. In Proceedings of the 3rd annual ACM conference on Research and development in information retrieval (pp. 35-56). Butterworth & Co..
〔14〕Rui, Y., Huang, T. S., Ortega, M., & Mehrotra, S. (1998). Relevance feedback: a power tool for interactive content-based image retrieval. IEEE Transactions on circuits and systems for video technology, 8(5), 644-655.
〔15〕Yan, R., Hauptmann, A., & Jin, R. (2003, July). Multimedia search with pseudo-relevance feedback. In International Conference on Image and Video Retrieval (pp. 238-247). Springer Berlin Heidelberg.
〔16〕Belkin, N., Cool, C., & Koenemann, J. (1996). On the potential utility of negative relevance feedback in interactive information retrieval. Proceedings of the 19th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR′96), Zurich, Switzerland.
〔17〕Kaptein, R., Kamps, J., & Hiemstra, D. (2008). The Impact of Positive, Negative and Topical Relevance Feedback. Proceedings of the 17th Text REtrieval Conference (TREC 2008), Gaithersburg, MD, USA.
〔18〕Meij, E., Weerkamp, W., He, J., & de Rijke, M. (2008). Incorporating Non-Relevance Information in the Estimation of Query Models. Proceedings of the 17th Text REtrieval Conference (TREC 2008), Gaithersburg, MD, USA.
〔19〕Wang, J., & Ye, X. (2010). The Study of Methods for Language Model Based Positive and Negative Relevance Feedback in Information Retrieval. Proceedings of the 2010 IEEE International Conference on Intelligent Computing and Intelligent Systems (ICIS 2010), 870-873, 29-31 Oct. 2010, Xiamen, China.
〔20〕Meij, E., He, J., Weerkamp, W., & de Rijke, M. (2009). Topical Diversity and Relevance Feedback. Proceedings of the 18th Text REtrieval Conference (TREC 2009), Gaithersburg, MD, USA.

指導教授

周世傑(Shih-Chieh Chou)

審核日期

2016-7-5

推文