相關回饋段落對於文件檢索效能之影響

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姓名

楊朝傑(Chao-Jie Yang) 查詢紙本館藏

畢業系所

資訊管理學系

論文名稱

相關回饋段落對於文件檢索效能之影響
(The Impact of Relevance Feedback Passages on Documents Retrieval Performance)

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至系統瀏覽論文 ( 永不開放)

摘要(中)

在文件檢索系統當中，透過相關回饋方法與查詢字詞擴張的應用，可使檢索系統的效能獲得一定程度的改善。但所謂的相關文件，其內容並非完全與使用者資訊需求相符，其中還是有若干部分是非相關或是無意義的雜訊區塊，而這些區塊內資訊就會影響查詢擴張後的字詞，使得擴張後的查詢仍無法獲得更佳的文件檢索準確率，因此本研究將運用相關回饋段落資訊來進行查詢擴張，並探討不同的段落挑選方式下對於文件檢索效能的影響。實驗中會先將相關回饋中的文件進行段落切割，並依據不同的段落挑選組合模式，將這些段落做為擴張查詢字詞的資訊來源，最後以第二次的文件檢索的準確率來探討不同段落大小與挑選方式造成的影響。
本研究的實驗結果顯示，藉由相關回饋當中段落資訊的應用，能讓檢索系統進行查詢擴張時排除掉更多非相關字詞，使得二次文件檢索的準確率有更佳的提升，且其改善的後的效果亦較以全文回饋的方式更為有效。

摘要(英)

In information retrieval systems, the system performance can be improved by the application of relevance feedback and query expansion. In fact, the contents of the relevant documents do not always match the information needs of the users. There are still several non-relevant and meaningless noise blocks in the documents and those noises will affect the performance of document retrieval. In this research, it attempts to eliminate the effect of the noises from relevance feedback by using passages information, and also discuss this impact of different passages combination on the performance of second document retrieval. In the experiments, our system first splits the contents of the documents into passages, and then selects passages according to the selection method to use in query expansion. Finally, we use the precision of second document retrieval to discuss the influence of different passage selection methods.
According to the results of experiments, the system performance can be improved by using passages information. The precision of second document retrieval which using passages information is better than the performance which using full-text information. This study has proved that the passages information is very useful on query expansion.

關鍵字(中)

★ 相關回饋
★ 段落挑選
★ 查詢擴張
★ 文件檢索

關鍵字(英)

★ query expansion
★ document retrieval
★ relevance feedback
★ passage selection

論文目次

論文摘要 i
Abstract ii
目錄 iii
圖目錄 v
表目錄 vi
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　　研究限制 3
1-4　　研究流程 3
1-5　　論文架構 4
第二章　文獻探討 5
2-1　　相關回饋 5
2-1-1　文件回饋方式 5
2-1-2　相關回饋應用探討 6
2-2　　段落相關研究 7
2-2-1　段落檢索與應用 7
2-2-2　段落切割方法 9
2-2-3　段落挑選方法 11
第三章　系統設計 15
3-1　　系統架構 15
3-2　　相關回饋段落處理 16
3-2-1　段落切割器 16
3-2-2　段落前處理器 17
3-2-3　段落挑選器 18
3-2-4　字詞選擇器 19
3-3　　檢索排名系統 20
3-3-1　相似度計算器 20
第四章　實驗分析 22
4-1　　系統平台與環境 22
4-2　　TREC（TEXT RETRIEVAL CONFERENCE）資料集 22
4-2-1　查詢主題（Topics） 23
4-2-2　文件集 24
4-3　　實驗設定 25
4-3-1　實驗評估準則 25
4-3-2　實驗對象 26
4-3-3　實驗參數設定 26
4-4　　實驗設計 27
4-4-1　實驗一 27
4-4-2　實驗二 30
4-4-3　實驗三 33
4-5　　總結與討論 35
第五章　結論 37
5-1　　研究結論與貢獻 37
5-2　　未來研究方向 38
參考文獻 40

參考文獻

[1] Allan, J. (1995). Relevance feedback with too much data. Paper presented at the Proceedings of the 18th annual international ACM SIGIR conference on Research and Development in Information Retrieval, Seattle, Washington, United States. 337-343.
[2] Baeza-Yates, R. A., & Ribeiro-Neto, B. (1999). Modern Information Retrieval: Addison-Wesley Longman Publishing Co., Inc.
[3] Callan, J. P. (1994). Passage-level evidence in document retrieval. Paper presented at the Proceedings of the 17th annual international ACM SIGIR conference on Research and Development in Information Retrieval, Dublin, Ireland. 302-310.
[4] Cormack, G. V., Clarke, C. L. A., Palmer, C. R., & To, S. S. L. (2000). Passage-based query refinement: (MultiText experiments for TREC-6). Information Processing & Management, 36(1), 133-153.
[5] Harman, D. (1992). 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.
[6] Hearst, M. A. (1997). TextTiling: segmenting text into multi-paragraph subtopic passages. Computational Linguistics, 23(1), 33-64.
[7] Kaszkiel, M., & Zobel, J. (1997). Passage retrieval revisited. SIGIR Forum, 31(SI), 178-185.
[8] Kaszkiel, M., & Zobel, J. (2001). Effective ranking with arbitrary passages. Journal of the American Society for Information Science and Technology, 52(4), 344-364.
[9] Krikon, E., Kurland, O., & Bendersky, M. (2010). Utilizing inter-passage and inter-document similarities for reranking search results. ACM Transactions on Information Systems, 29(1), 1-28.
[10] Liu, X., & Croft, W. B. (2002). Passage retrieval based on language models. Paper presented at the Proceedings of the 11th international conference on Information and Knowledge Management, McLean, Virginia, USA. 375-382.
[11] Lv, Y., & Zhai, C. (2010). Positional relevance model for pseudo-relevance feedback. Paper presented at the Proceeding of the 33rd international ACM SIGIR conference on Research and Development in Information Retrieval, Geneva, Switzerland. 579-586.
[12] Na, S.-H., Kang, I.-S., Lee, Y.-H., & Lee, J.-H. (2008). Applying completely-arbitrary passage for pseudo-relevance feedback in language modeling approach. Paper presented at the Proceedings of the 4th Asia Information Retrieval conference on Information Retrieval Technology, Harbin, China. 626-631.
[13] Porter, M. F. (1980). An algorithm for suffix stripping. Program: electronic library and information systems, 14(3), 130-137.
[14] Qiu, Y., & Frei, H.-P. (1993). Concept based query expansion. Paper presented at the Proceedings of the 16th annual international ACM SIGIR conference on Research and Development in Information Retrieval, Pittsburgh, Pennsylvania, United States. 160-169.
[15] Quiroga, L. M., & Mostafa, J. (2002). An experiment in building profiles in information filtering: the role of context of user relevance feedback. Information Processing & Management, 38(5), 671-694.
[16] Rocchio, J. J. (1971). Relevance feedback in information retrieval. In G. Salton (Ed.), The SMART Retrieval System: Experiments in Automatic Document Processing (pp. 313-323): Prentice-Hall, Englewood Cliffs NJ.
[17] Singhal, A., Buckley, C., & Mitra, M. (1996). Pivoted document length normalization. Paper presented at the Proceedings of the 19th annual international ACM SIGIR conference on Research and Development in Information Retrieval, Zurich, Switzerland. 21-29.
[18] Salton, G., & Lesk, M. E. (1968). Computer Evaluation of Indexing and Text Processing. Journal of the ACM, 15(1), 8-36.
[19] Salton, G., Wong, A., & Yang, C. S. (1975). A vector space model for automatic indexing. Communications of the ACM, 18(11), 613-620.
[20] Tari, L., Tu, P. H., Lumpkin, B., Leaman, R., Gonzalez, G., & Baral, C. (2007). Passage relevancy through semantic relatedness, TREC (Vol. Special Publication 500-274): National Institute of Standards and Technology (NIST).
[21] Wilkinson, R. (1994). Effective retrieval of structured documents. Paper presented at the Proceedings of the 17th annual international ACM SIGIR conference on Research and Development in Information Retrieval, Dublin, Ireland. 311-317.
[22] Xu, J., & Croft, W. B. (1996). 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.
[23] Yang, K., Maglaughlin, K. L., & Newby, G. B. (2001). Passage feedback with IRIS. Information Processing & Management, 37(3), 521-541.
[24] CLEF stop word list. Retrieved June, 15, 2011, from the World Wide Web: http://members.unine.ch/jacques.savoy/clef/englishST.txt
[25] TREC. Please refer to TREC Web site: http://trec.nist.gov/

指導教授

周世傑(Shih-Chieh Chou)

審核日期

2011-7-5

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