博碩士論文 89443009 詳細資訊




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:7 、訪客IP:3.144.3.63
姓名 張維平(Weiping Chang)  查詢紙本館藏   畢業系所 資訊管理學系
論文名稱 向量空間模式系統中對於檢索文件相關回饋之研究
(A study of relevance feedback on retrieved documents in a vector-space-modeled system)
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]   [檢視]  [下載]
  1. 本電子論文使用權限為同意立即開放。
  2. 已達開放權限電子全文僅授權使用者為學術研究之目的,進行個人非營利性質之檢索、閱讀、列印。
  3. 請遵守中華民國著作權法之相關規定,切勿任意重製、散佈、改作、轉貼、播送,以免觸法。

摘要(中) 在向量空間模式資訊擷取系統上,相關回饋是一種應用於提升擷取效率的技術。相關回饋的技術係在檢索過程中,由使用者對於系統檢索出的文件,進行相關或不相關的評估。過去的研究,主要在運用使用者回饋的資訊,修改使用者的興趣向量。本研究找出,過去研究在使用者相關或不相關的回饋文件中,未完全被研究過的資訊。這些資訊是關於字詞在相關或不相關文件中,所出現的各種狀態。本研究發展一個實驗性的資訊擷取系統與方法,以展示對於字詞出現狀態資訊的應用,並進行相關實驗,以研究這些方法是否具有效果。本研究實驗的結果顯示,字詞出現狀態的資訊是可以被抽取出來,並可應用於提升擷取效率。
摘要(英) Relevance feedback is one of the techniques applied in a vector-space-modeled Information Retrieval (IR) system to enhance retrieval effectiveness. The feedback process usually has the user rate the documents retrieved as relevant or non-relevant. Most past studies apply the information of document relevance to the modification of the vector that is used to manifest the user’s information interest. In this study, we have identified additional information obtained from relevance feedback that was not fully studied in the past from the rated relevant/non-relevant documents for application. The information pertains to is about the situations of term appearances in the relevant/non-relevant documents. We have developed a method together with an IR system to demonstrate the application of the information of term appearance situation. Experiments have also been conducted to study its effect. The experimental results preliminarily show that the information of the term appearance situation could be extracted and appropriately applied to enhance retrieval effectiveness.
關鍵字(中) ★ 詞語權重
★ 敏感度
★ 資訊擷取
★ 詞頻
★ 相關回饋
★ 全球資訊網
關鍵字(英) ★ Information Retrieval
★ Relevance Feedback
★ Term Frequency
★ Sensitivity
★ Term Reweighting
★ World Wide Web
論文目次 Abstract i
Acknowledgement iii
Index iv
Figure index v
Table index vi
1. Introduction 1
2. Relevance Feedback 3
3. The Experimental Vector-Space-Modeled System 12
3.1 System framework 12
3.2 System flow of EIRS 19
4. Experiments and Results 22
4.1 Experiment process 22
4.2 Experimental Results 23
4.2.1 Experiments for system factor adjustment 24
4.2.2 Study of the effect of sensitivity 27
4.2.3 Study of the effect of negative user profile 28
5. Conclusion 30
References 32
Appendix A: Stopword List 35
參考文獻 [1] Azimi-Sadjadi, M., Salazar, J., Srinivasan, S., and Sheedvash, S., “An adaptable connectionist text retrieval system with relevance feedback”, Proceedings of the 2004 IEEE International Joint Conference on Neural Networks, Vol. 1, pp. 309-314, Budapest, Hungary, July 2004.
[2] Balabanovic, M., “An Adaptive Web Page Recommendation Service”, Proceedings of the First International Conference on Autonomous Agents, pp. 378-385, New York, February 1997.
[3] Biron, P., and Kraft, D., “New Methods for Relevance Feedback: Improving Information Retrieval Performance”, Proceedings of the ACM Symposium on Applied Computing, pp. 482-487, Nashville, Tennessee, February 1995.
[4] Buckley, C. and Salton, G., “Optimization of relevance feedback weights”, Proceedings of the Eighteenth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 351-357, Seattle, Washington, 1995.
[5] Choi, J., Kim, M., and Raghavan, V., “Adaptive relevance feedback method of extended Boolean model using hierarchical clustering techniques”, Information Processing and management, Vol. 42, No. 2, pp. 331-349, March 2006.
[6] Christiansen, A., and Lee, D., “Relevance feedback query refinement for PDF medical journal articles”, Proceedings of the 19th IEEE Symposium on Computer-Based Medical Systems, pp. 57-62, Salt Lake City, Utah, June 2006.
[7] Crestani, F., “Neural relevance feedback for information retrieval”, In B. Bouchon-Meunier, B., Yager, R., & Zadeh, L., (Eds.), Uncertainty in intelligent in information systems, World Scientific, Singapore, 2000.
[8] Desjardins, G. and Godin, R., “Combining Relevance Feedback and Genetic Algorithms in an Internet Information Filtering Engine”, Proceedings of RIAO2000 Conference, Vol. 2, pp. 1676-1685, Paris, France, April 2000.
[9] Dillon, M. and Desper, J., “Automatic Relevance Feedback in Boolean Retrieval System”, Journal of Documentation, Vol. 36, pp. 197-208, 1980.
[10] Drucker, H., Shahary, B., and Gibbon, D., “Relevance Feedback using Support Vector Machines”, Proceedings of the 18th International Conference on Machine Learning (ICML) , pp. 122-129, Williamstown, MA, June 2001.
[11] Ekkelenkamp, R., Kraaij, W., and Leeuwen, D., “TNO TREC7 Site Report: SDR and Filtering”, Proceedings of the Seventh Text REtrieval Conference, pp. 455-462, Gaithersburg, Maryland, November 1998.
[12] Graugaard, L., “Implicit relevance feedback in interactive music: issues, challenges, and case studies”, Proceedings of the 1st international conference on Information interaction in context, pp. 119-128, Copenhagen, Denmark, October 2006.
[13] Harman, D., “Relevance Feedback Revised”, Proceedings of 15th Annual International ACM SIGIR Conference, pp. 1-10, New York, June 1992.
[14] Hoashi, K., Matsumoto, K., Inoue, N., and Hashimoto, K., “Document Filtering Method Using Non-Relevant Information Profile”, Proceedings of ACM SIGIR 2000, pp. 176-183, Athens, Greece, July 2000.
[15] Hoashi, K., Zeitler, E., Inoue, N., “Implementation of Relevance Feedback for Content-based Music Retrieval Based on User Preferences”, Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 385-386, Tampere, Finland, August 2002.
[16] Hoeber, O., and Yang, X., “Interactive Web Information Retrieval Using WordBars”, Proceedings of 2006 IEEE/WIC/ACM International Conference on Web Intelligence, pp. 875-882, Hong-Kong, December 2006.
[17] Ide, E., “New Experiments in Relevance Feedback”, In Salton G.(Ed.), The SMART Retrieval System: Experiments in automatic Document Processing, pp. 337-354, Prentice-Hall, Englewood Cliffs, NJ, 1971.
[18] Justino, E., Bortolozzi, F., and Sabourin, R., “A comparison of SVM and HMM classifiers in the off-line signature verification”, Pattern Recognition Letters, Vol. 26, No. 9, pp. 1377-1385, July 2005.
[19] Kim, B., Kim, J., and Kim, J., “Query term expansion and reweighting using term co-occurrence similarity and fuzzy inference”, Proceedings of IFSA World Congress and 20th NAFIPS International Conference, Vol. 2, pp. 715-720, Vancouver, Canada, July 2001.
[20] Li, F., Mehlitz, M., Feng, L., and Sheng, H., “Web Pages Clustering and Concept Mining: An Approach Intelligent Information Retrieval”, Technical Program of 2006 IEEE International Conferences on Cybernetics & Intelligent Systems (CIS) and Robotics, Automation & Mechatronics (RAM), Bangkok, Thailand, June 2006.
[21] Moyotl, E., and Jimenez, H., “An Analysis on Frequency of Terms for Text Categorization”, Proceedings of Conference of Spanish Natural Language Processing Society, pp. 141-146, Barcelona, July 2004.
[22] Navigli, R., and Velardi, P., “An analysis of ontology-based query expansion strategies”, Proceedings of Workshop on Adaptive Text Extraction and Mining in the 14th ECML, pp. 42-49, Croatia, September 2003.
[23] Nick, Z., and Themis, P., “Web Search Using a Genetic Algorithm”, IEEE Internet Computing, Vol. 5, No. 2, pp. 18-26, March/April 2001.
[24] Ng, H., Ang, H., and Soon, W., “DSO at TREC-8: A Hybrid Algorithm for the Routing Task”, Proceedings of the Eighth Text REtrieval Conference (TREC-8), pp. 267, Gaithersburg, Maryland, November 1999.
[25] Rho, S., Hwang, E., and Kim, M., “Music Information Retrieval Using a GA-based Relevance Feedback”, Proceedings of 2007 International Conference on Multimedia and Ubiquitous Engineering (MUE'07) , pp. 739-744, Seoul, Korea, April, 2007.
[26] Robertson, S., and Sparck-Jones, K., “Relevance Weighting of search terms”, Journal of the American Society for Information Science, Vol. 27, No.3, pp. 129-146, May/June 1976.
[27] Rocchio, J., Document retrieval systems – Optimization and evaluation, Unpublished doctoral dissertation, Harvard University, Cambridge, MA, USA, March 1966.
[28] Salton, G., and Buckley, C., “Term weighting approaches in automatic text retrieval”, Information Processing and Management, Vol. 24, pp. 513-523, Nov. 1988.
[29] Salton, G., Fox, E., and Wu, H. “Extended Boolean information retrieval”, Communication of the ACM, Vol. 26, No.11, pp. 1022-1036, November 1983.
[30] Savoy, J., “Data Fusion for Effective European Monolingual Information Retrieval”, Working Notes for the Cross Language Evaluation Forum (CLEF) 2004 Workshop, pp. 233-244, Bath, UK, September 2004.
[31] Shin,K., Han, S., Gelbukh, A., and Park, J., “Advanced Relevance Feedback Query Expansion Strategy for Information Retrieval in MEDLINE”, LNCS Vol. 3287, pp. 425-431, October 2004.
[32] Singhal, A., Mitra, M., and Buckley, C., “Learning routing queries in a query zone”, Proceedings of the Twentieth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 25-32, Philadelphia, July 1997.
[33] Vires, A., and Roelleke, T., “Relevance Information: A Loss of Entropy but a Gain for IDF?”, Proceedings of SIGIR'05, pp. 282-289, Salvador, Brazil, August 2005.
[34] Wei, C., and Li, C., “Learning Pathological Characteristics from User’s Relevance Feedback for Content-Based Mammogram Retrieval”, Proceedings of Eighth IEEE International Symposium on Multimedia (ISM'06), pp. 738-741, San Diego, CA, December 2006.
[35] Xu, X., Lee, D., Antani, S., and Long, L., “Relevance feedback for spine X-ray retrieval”, Proceedings of the 18th International Symposium on Computer-Based Medical Systems, pp. 197-202, Dublin, Ireland, June 2005.
指導教授 周世傑(Shihchieh Chou) 審核日期 2007-7-10
推文 facebook   plurk   twitter   funp   google   live   udn   HD   myshare   reddit   netvibes   friend   youpush   delicious   baidu   
網路書籤 Google bookmarks   del.icio.us   hemidemi   myshare   

若有論文相關問題,請聯絡國立中央大學圖書館推廣服務組 TEL:(03)422-7151轉57407,或E-mail聯絡  - 隱私權政策聲明