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研究生:王仁邦
研究生(外文):Ren-Bang Wang
論文名稱:以字詞相互關係的擴張查詢方式來增加檢索效益
論文名稱(外文):A Query Expansion Approach Based on Word Correlations to Improve the Retrieval Effectiveness
指導教授:黃胤傅
指導教授(外文):Yin-Fu Huang
學位類別:碩士
校院名稱:國立雲林科技大學
系所名稱:資訊工程研究所
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:2008
畢業學年度:96
語文別:中文
論文頁數:50
中文關鍵詞:搜尋引擎字詞使用差異字詞相互關係資訊檢索擴張查詢
外文關鍵詞:word mismatchsearch engineword correlationinformation retrievalquery expansion
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  • 下載下載:34
  • 收藏至我的研究室書目清單書目收藏:3
近來電腦及網路的快速發展,使得搜尋引擎成為尋找資訊重要的平台,然而,字詞使用差異在資訊檢索中是一項挑戰,我們透過擴張查詢的技術來解決此問題。在此篇論文中,我們取得相關網頁的標題與關鍵字作為資料來源並建立為文件資料庫。我們主要是利用兩個關鍵字共同出現在同一篇文件為基礎的方式來做擴張查詢。在實驗中,可以發現我們的方法能夠獲得更多相關的文件,在複合查詢中也能夠充分的表現出來。
Nowadays, the rapid development of computers and the Internet makes search engines as important platforms to search desired information. However, the major challenge in information retrieval is the word mismatch. In order to solve the word mismatch, a query expansion technique that extends the keywords specified in initial queries is proposed. In the paper, we retrieve the titles and keywords of related web pages as the data source to build the document database. Then, we use specified keywords to find other keywords co-occurring in the same documents, and then find out more relevant documents. In the experiments, it reveals that our method could provide more relevant documents even for a complex query.
中文摘要 ------------------------------------------------------------------------------------ i
英文摘要 ------------------------------------------------------------------------------------ ii
誌謝 ------------------------------------------------------------------------------------------ iii
目錄 ------------------------------------------------------------------------------------------ iv
表目錄 --------------------------------------------------------------------------------------- v
圖目錄 --------------------------------------------------------------------------------------- vi
一、 緒論------------------------------------------------------------------------------------ 1
二、 相關工作------------------------------------------------------------------------------ 2
2.1 擴張查詢------------------------------------------------------------------------ 2
2.2 全域分析------------------------------------------------------------------------ 2
2.3 局部分析------------------------------------------------------------------------ 3
三、 系統架構------------------------------------------------------------------------------ 4
3.1 建立文件資料庫--------------------------------------------------------------- 4
3.2 搜尋相關文件------------------------------------------------------------------ 6
四、 搜尋相關文件演算法--------------------------------------------------------------- 9
4.1 找出相關關鍵字--------------------------------------------------------------- 9
4.2 找出Frequent Keywords------------------------------------------------------ 10
4.3 找出Correlated Keywords---------------------------------------------------- 11
4.4 找出相關的文件--------------------------------------------------------------- 12
4.5 搜尋相關文件------------------------------------------------------------------ 13
五、 實驗與評估--------------------------------------------------------------------------- 14
5.1 WebSearchEngine-------------------------------------------------------------- 14
5.2 相關性分析--------------------------------------------------------------------- 17
六、 結論------------------------------------------------------------------------------------ 18
參考文獻 ------------------------------------------------------------------------------------ 19
[1]R. Baeza-Yates and B. Ribeiro-Neto, Modern Information Retrieval, Addison-Wesley, 1999.
[2]C. Buckley, G. Salton, J. Allan, and A. Singhal, “Automatic query expansion using SMART,” Proc. the 3rd Text Retrieval Conference, pp. 69-80, 1995.
[3]C. Buckley, M. Mitra, J. Walz, and C. Cardie, “Using clustering and superconcepts within SMART,” Proc. the 6th Text Retrieval Conference, pp. 107-124, 1998.
[4]S. Deerwester, S. T. Dumais, G. W. Furnas, T. K. Landauer, and R. A. Harshman, “Indexing by latent semantic analysis,” Journal of the American Society for Information Science, Vol. 41, No. 6, pp. 391-407, 1990.
[5]G. W. Furnas, et al., “Information retrieval using a singular value decomposition model of latent semantic structure,” Proc. the 11th Annual ACM SIGIR International Conference on Research and Development in Information Retrieval, pp. 465-480, 1988.
[6]K. S. Jones, Automatic Keyword Classification for Information Retrieval, Butterworths, London, 1971.
[7]A. Lu, M. Ayoub, and J. Dong, “Ad hoc experiments using EUREKA,” Proc. the 5th Text Retrieval Conference, pp. 229-240, 1997.
[8]M. Mitra, A. Singhal, and C. Buckley, “Improving automatic query expansion,” Proc. the 21st Annual ACM SIGIR International Conference on Research and Development in Information Retrieval, pp. 206-214, 1998.
[9]J. J. Rocchio, “Relevance feedback in information retrieval,” in The SMART Retrieval System - Experiments in Automatic Document Processing, Prentice Hall, pp. 313-323, 1971.
[10]G. Salton and C. Buckley, “Improving retrieval performance by relevance feedback,” Journal of the American Society for Information Science, Vol. 41, No. 4, pp. 288-297, 1990.
[11]J. Xu and W. B. Croft, “Query expansion using local and global document analysis,” Proc. the 19th Annual ACM SIGIR International Conference on Research and Development in Information Retrieval, pp. 4-11, 1996.
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