應用相關回饋之語詞資訊於查詢擴展之方法

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：52

、訪客IP：18.118.12.102

姓名

林義翔(Yi-Siang Lin) 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資訊於查詢擴展之方法
(The application of the term information residing in relevance feedback for query expansion)

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)

資訊檢索 (Information Retrieval)系統在人們的生活中已經是一個不可或缺的重要工具，相關回饋 (Relevance Feedback)的領域中Rocchio演算法因實作簡單且具有一定的效能，所以經常被廣泛的使用與研究，其概念為分析相關回饋結果中字詞的重要程度，作為挑選查詢擴展 (Query Expansion)字詞之依據，使資訊檢索系統更貼近使用者的資訊需求，但是其僅單純著重於字詞的出現頻率作為文件相關與否的依據，沒有考慮到字詞之間是否存在其他可以善加利用的資訊，而且在真實世界中出現頻率最高的字詞不一定與使用者的資訊需求具有相關性。因此本研究運用相關回饋的概念，分析相關文件中字詞之間所隱含的語意關係與共現關係，萃取其中適合的相關字詞作為查詢擴展之字詞來源，目的在於使查詢關鍵字能更符合使用者之資訊需求，解決過往相關回饋僅考慮字詞出現頻率而忽略的語詞資訊，並透過實驗證明本研究所提出之方法與其他方法相比之下，皆能有不錯的檢索效果，達到提升文件檢索準確率之最終目的。

摘要(英)

Information retrieval systems are an indispensable tool in people′s lives. Rocchio’s query expansion method is simple and effective in the analyzing of the importance of terms residing in relevance feedback. However, in the make up of terms and its importance for query expansion, Rocchio’s method only focuses on term frequency and ignores other relationships between terms. Therefore, this study is aimed to develop a method in the utilization of the information of relevance feedback to analyze the semantic and co-occurrence relationships of terms in relevant documents to extract adequate relevant terms for query expansion. The results of experiments show that the proposed method of this study 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)

★ Information Retrieval
★ Relevance Feedback
★ Query Expansion

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 3
1-4 論文架構 3
二、文獻探討 4
2-1 相關回饋 (Relevance Feedback) 4
2-1-1 相關回饋背景與應用 4
2-1-2 Rocchio演算法 6
2-2 查詢擴展 (Query Expansion) 8
2-2-1 局部查詢擴展 (Local Query Expansion) 9
2-2-2 全域查詢擴展 (Global Query Expansion) 10
2-3 WordNet 10
2-4 語意註解應用方法 13
2-5 正規化Google距離 (Normalized Google Distance, NGD) 15
三、研究方法 17
3-1 系統架構 17
3-2 方法設計 18
四、實驗設計 25
4-1 實驗資料 25
4-2 實驗評估指標 29
4-3 實驗之查詢主題設定 32
4-4 實驗流程 33
4-4-1 實驗一 34
4-4-2 實驗二 34
4-5 實驗結果 35
4-5-1 實驗一結果 35
4-5-2 實驗二結果 42
4-6 實驗結果討論 50
五、結論 52
5-1 結論與貢獻 52
5-2 未來研究方向 53
參考文獻 54

參考文獻

Araujo, L. & Pérez-Agüera, J. R. (2008, March 26-28). Improving query expansion with stemming terms: a new genetic algorithm approach. Paper presented at the EvoCOP′08 Proceedings of the 8th European conference on Evolutionary computation in combinatorial optimization, Naples, Italy, 182-193.
Banerjee, S. & Pedersen, T. (2003, August 9-15). Extended gloss overlaps as a measure of semantic relatedness. Paper presented at the International Joint Conference on Artificial Intelligence (IJCAI), Acapulco, Mexico, 3, 805-810.
Bhogal, J., Macfarlane, A., & Smith, P. (2007). A review of ontology based query expansion. Information Processing & Management, 43(4), 866-886.
Bird, S., Klein, E., & Loper, E. (2009, June). The WordNet Hierarchy. Natural language processing with Python (Vol. 1, pp. 67-70). California, USA: O′Reilly Media.
Buckley, C. & Salton, G. (1995, July 9-13). Optimization of relevance feedback weights. Paper presented at the Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval, Seattle, USA, 351-357.
Chen, Q., Li, M., & Zhou, M. (2007, June 28-30). Improving Query Spelling Correction Using Web Search Results. Paper presented at the Empirical Methods in Natural Language Processing Conference on Computational Natural Language Learning (EMNLP-CoNLL), Prague, Czech Republic, 7, 181-189.
Cilibrasi, R. & Vitanyi, P. (2004). Automatic meaning discovery using Google. Retrieved 04/22, 2015, from http://homepages.cwi.nl/~paulv/papers/amdug.pdf
Cilibrasi, R. L. & Vitanyi, P. M. (2007). The google similarity distance. IEEE Transactions on Knowledge and Data Engineering, 19(3), 370-383.
Crouch, C. J. (1990). An approach to the automatic construction of global thesauri. Information Processing & Management, 26(5), 629-640.
Dang, C. & Luo, X. (2008, April 6-8). WordNet-based document summarization. Paper presented at the Proceeding of the 7th WSEAS International Conference on Applied Computer & Applied Computational Science (ACACOS′08), Hangzhou, China, 383-387.
Davis, J. & Goadrich, M. (2006, June 25-29). The relationship between Precision-Recall and ROC curves. Paper presented at the Proceedings of the 23rd international conference on Machine learning, Carnegie Mellon University, Pittsburgh, USA, 233-240.
Dillon, M. & Desper, J. (1980). The use of automatic relevance feedback in Boolean retrieval systems. Journal of Documentation, 36(3), 197-208.
Evangelista, A. & Kjos-Hanssen, B. (2009). Google distance between words. Computing Research Repository (CoRR), 0901.4180.
Everingham, M., Van Gool, L., Williams, C. K., Winn, J., & Zisserman, A. (2010). The pascal visual object classes (voc) challenge. International journal of computer vision, 88(2), 303-338.
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.
Gay, G., Haiduc, S., Marcus, A., & Menzies, T. (2009, September 20-26). On the use of relevance feedback in IR-based concept location. Paper presented at the IEEE International Conference on Software Maintenance (ICSM 2009), Edmonton, Canada, 351-360.
Harman, D. (1992, June 21-24). Relevance feedback revisited. Paper presented at the Proceedings of the 15th annual international ACM SIGIR conference on Research and development in information retrieval, Copenhagen, Denmark, 1-10.
Harman, D. K. (1993). The first text retrieval conference (TREC-1) Rockville, MD, USA, 4–6 November, 1992. Information Processing & Management, 29(4), 411-414.
Jesse, A. & Nissan, H. (2008). We knew the web was big... Retrieved 05/01, 2015, from http://googleblog.blogspot.tw/2008/07/we-knew-web-was-big.html
Kelly, D. & Belkin, N. J. (2001, September 9-12). Reading time, scrolling and interaction: exploring implicit sources of user preferences for relevance feedback. Paper presented at the Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval, New Orleans, USA, 408-409.
Kelly, D. & Teevan, J. (2003, July 28 - August 1). Implicit feedback for inferring user preference: a bibliography. Paper presented at the ACM SIGIR Forum, Toronto, Canada, 37(2), 18-28.
Lemur Project Group. The Lemur Project. Retrieved 05/01, 2015, from http://www.lemurproject.org/
Liu, H.-Y. (2014). The application of using semantic analysis techniques in relevance feedback to the document re-ranking. National Central University, Taiwan.
Manning, C. D., Raghavan, P., & Schütze, H. (2008, July). Introduction to information retrieval (Vol. 1, pp. 178-189). Cambridge, England: Cambridge university press.
Miller, G. & Fellbaum, C. (1998, May). Wordnet: An electronic lexical database (pp. 274-281). Cambridge, England: MIT Press.
Miller, G. A. (1995). WordNet: a lexical database for English. Communications of the ACM, 38(11), 39-41.
Miller, G. A., Beckwith, R., Fellbaum, C., Gross, D., & Miller, K. J. (1990). Introduction to wordnet: An on-line lexical database. International journal of lexicography, 3(4), 235-244.
Moldovan, D. & Novischi, A. (2004). Word sense disambiguation of WordNet glosses. Computer Speech & Language, 18(3), 301-317.
Pinto, F. J. & Pérez-Sanjulián, C. F. (2008). Automatic query expansion and word sense disambiguation with long and short queries using WordNet under vector model. Actas de los Talleres de las Jornadas de Ingeniería del Software y Bases de Datos, 2(2), 17-23.
Porter, M. F. (1980). An algorithm for suffix stripping. Program, 14(3), 130-137.
Potts, K. (2007, September). Web Design and Marketing Solutions for Business Websites (pp. 287-288). New York, USA: Apress.
Robertson, S. E., Van Rijsbergen, C. J., & Porter, M. F. (1980). Probabilistic models of indexing and searching. Paper presented at the Proceedings of the 3rd annual ACM conference on Research and development in information retrieval, Cambridge, England, 35-56.
Rocchio. (1971). Relevance feedback in information retrieval. In: The SMART Retrieval System Experiments in Automatic Document Processing. In G. Salton (Ed.), (pp. 313-323). New Jersey, USA: Prentice-Hall.
Rui, Y., Huang, T. S., & Mehrotra, S. (1997, October 26-29). Content-based image retrieval with relevance feedback in MARS. Paper presented at the IEEE International Conference on Image Processing (ICIP 1997), California, USA, 2, 815-818.
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.
Salton, G. (1971). The SMART retrieval system—experiments in automatic document processing (pp. 316-329). New Jersey, USA: Prentice-Hall.
Salton, G. & Lesk, M. E. (1968). Computer evaluation of indexing and text processing. Journal of the ACM (JACM), 15(1), 8-36.
Salton, G. & McGill, M. J. (1983, September ). Introduction to modern information retrieval (pp. 177-194). New York, USA: McGraw-Hill.
Scott, S. & Matwin, S. (1998, August 16). Text classification using WordNet hypernyms. Paper presented at the Use of WordNet in natural language processing systems: Proceedings of the conference, Montréal, Canada, 38-44.
Shi, Z., Gu, B., Popowich, F., & Sarkar, A. (2005, October). Synonym-based query expansion and boosting-based re-ranking: A two-phase approach for genomic information retrieval. Paper presented at the the Fourteenth Text REtrieval Conference (TREC 2005), NIST, Gaithersburg, Maryland, USA.
Sihvonen, A. & Vakkari, P. (2004). Subject knowledge improves interactive query expansion assisted by a thesaurus. Journal of Documentation, 60(6), 673-690.
Statistic Brain Research Institute. Google Annual Search Statistics. Retrieved 05/01, 2015, from http://www.statisticbrain.com/google-searches/
Text Retrieval Conference. Topic Creation. Retrieved 05/01, 2015, from http://trec.nist.gov/presentations/TREC6/11.html
The Apache Software Foundation. Lucene. Retrieved 05/01, 2015, from http://lucene.apache.org/
Turpin, A. & Scholer, F. (2006, August 6-10). User performance versus precision measures for simple search tasks. Paper presented at the Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval, Seattle, USA, 11-18.
Vechtomova, O. & Wang, Y. (2006). A study of the effect of term proximity on query expansion. Journal of Information Science, 32(4), 324-333.
Voorhees, E. M. (1993, June 27 - July 1). Using WordNet to disambiguate word senses for text retrieval. Paper presented at the Proceedings of the 16th annual international ACM SIGIR conference on Research and development in information retrieval, Pittsburgh, USA, 171-180.
Voorhees, E. M. & Harman, D. (2000). Overview of the sixth text retrieval conference (TREC-6). Information Processing & Management, 36(1), 3-35.
Wu, I.-C., Lin, Y.-S., & Liu, C.-H. (2011). An exploratory study of navigating wikipedia semantically: model and application. Online Communities and Social Computing (pp. 140-149). Germany: Springer.
Xiao-Gang, W. & Yue, L. (2009, July 11-12). Web Personalization Method Based on Relevance Feedback on Keyword Space. Paper presented at the IITA International Conference on Services Science, Management and Engineering (SSME 2009), 34-37.
Xu, J. & Croft, W. B. (1996, August 18-22). Query expansion using local and global document analysis. Paper presented at the Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval, Zurich, Switzerland, 4-11.
Yan, R., Hauptmann, A., & Jin, R. (2003). Multimedia search with pseudo-relevance feedback. Image and Video Retrieval (pp. 238-247). Germany: Springer.
Yang, Y., Nie, F., Xu, D., Luo, J., Zhuang, Y., & Pan, Y. (2012). A multimedia retrieval framework based on semi-supervised ranking and relevance feedback. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(4), 723-742.
Zhu, M. (2004). Recall, precision and average precision. Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, Canada, 2.

指導教授

周世傑(Shih-Chieh Chou)

審核日期

2015-7-15

推文