應用相關回饋之醫學字詞資訊於醫學查詢擴展之方法

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：25

、訪客IP：3.142.134.166

姓名

曹盛焱(Sheng-Yen Tsao) 查詢紙本館藏

畢業系所

資訊管理學系

論文名稱

應用相關回饋之醫學字詞資訊於醫學查詢擴展之方法
(The application of the medical term information residing in relevance feedback for medical 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)

在這數位化的時代醫學文獻、病歷、臨床紀錄等都已朝數位化的方向發展，每日所增長的量非常巨大，要如何有效管理及在必要時刻快速搜尋這些文件已成醫學檢索領域中重要的議題，但大多數使用者在搜尋時常因語意表達不清或是用詞模糊導致系統無法回傳有效的資訊給使用者。使用相關回饋(Relevance Feedback)的查詢擴展(Query Expansion)方法一直解是決這問題的主要方法之一，其中最著名的Rocchio演算法僅以字詞間的頻率來判斷，未考量字詞間其他可利用的資訊及專有名詞的重要性，因此本研究利用原始查詢與查詢結果作為基礎，主要利用Word2Vec模型所建立之醫學字詞向量以及MeSH主題詞表來分析相關回饋以及查詢字詞間所隱含之語意關係，萃取出相關回饋資訊內關鍵字詞，並利用MeSH主題字詞表以及Word2Vec模型進行字詞擴展，將其加入最後的查詢擴展集合，使查詢能更準確的回傳符合使用者需求之結果。本研實驗使用TREC 2007 Genomics資料集進行檢索效能驗證，最終結果統計本研究所提出之應用相關回饋之醫學字詞資訊於醫學查詢擴展之方法相較於Rocchio演算法在評估指標P@5提升20%、P@10提升11%、P@N提升13%、MAP提升17%以及PR Curve提升14%，顯示本研究檢索結果能更符合使用者需求。

摘要(英)

The electronization of medical literature, medical record and clinical records are required in the information age. How to efficiently manage and search these huge data volume has become important issue in in medical retrieval domain. However, the search result sometimes is bad because user cannot effectively conversion his requirement to search keyword. Query expansion of Relevance feedback and is main method to solve this problem. Rocchio’s query expansion is most famous in relevance feedback. However, Rocchio’s method only focuses on term frequency and ignores other relationships between terms and medical terms. Therefore, this study is based on the user′s original query and search results, our research uses the medical word embeddings by the Word2Vec model and the MeSH to analyze the semantic relationship between the relevant feedback and the query words, extract the important terms in the relevant feedback information, and use the MeSH and Word2Vec model for query expansion. This study used TREC 2007 Genomics dataset for performance evaluation of retrieval. The experimental results show that the application of the medical term information residing in relevance feedback for medical query expansion can improve the retrieval performance.

關鍵字(中)

★ 資訊檢索
★ 相關回饋
★ 醫學查詢擴展
★ MeSH
★ Word2Vec

關鍵字(英)

★ Information Retrieval
★ Medical Query Expansion
★ Relevance Feedback
★ Word2Vec
★ MeSH

論文目次

論文摘要 i
ABSTRACT ii
誌謝 iii
目錄 iv
圖目錄 vi
表目錄 viii
一、前言 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
1-3-1 研究範圍 2
1-3-2 研究限制 3
1-4 論文架構 3
二、文獻探討 4
2-1 查詢擴展（QUERY EXPANSION） 4
2-1-1 局部查詢擴展 (Local Query Expansion) 5
2-1-2 全域查詢擴展(Global Query Expansion) 5
2-2 相關回饋（RELEVANCE FEEDBACK） 6
2-2-1 相關回饋背景與應用 6
2-2-2 Rocchio演算法 8
2-3 TF-IDF 9
2-4 WORD2VEC介紹 10
2-5 醫學字詞向量發展 12
2-6 MESH (MEDICAL SUBJECT HEADINGS) 13
三、研究方法 15
3-1 系統架構 15
3-2 方法設計 16
3-2-1 原始查詢結果處理 17
3-2-2 重要字詞處理 17
3-2-3 字詞語意分析處理 19
3-2-4 相關字詞語意擴展 21
四、實驗設計 23
4-1 實驗環境 23
4-2 實驗資料集 23
4-3 實驗評估指標 26
4-4 實驗流程 29
4-4-1 實驗一 29
4-4-2 實驗二 31
4-5 實驗結果 31
4-5-1 實驗一結果 31
4-5-2 實驗二結果 39
4-6 實驗結果討論 47
五、結論 49
5-1 結論與貢獻 49
5-2 未來研究方向 50
參考文獻 51

參考文獻

[1] A. L. Cochrane, Effectiveness and efficiency: random reflections on health services. Nuffield Provincial Hospitals Trust London, 1972.
[2] M. Crespo Azcárate, J. Mata Vázquez, and M. Maña López, "Improving image retrieval effectiveness via query expansion using MeSH hierarchical structure," Journal of the American Medical Informatics Association, vol. 20, no. 6, pp. 1014-1020, 2012.
[3] N. C. Ide, R. F. Loane, and D. Demner-Fushman, "Essie: a concept-based search engine for structured biomedical text," Journal of the American Medical Informatics Association, vol. 14, no. 3, pp. 253-263, 2007.
[4] W. Zhou, C. Yu, N. Smalheiser, V. Torvik, and J. Hong, "Knowledge-intensive conceptual retrieval and passage extraction of biomedical literature," in Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval, 2007, pp. 655-662: ACM.
[5] G. Salton and M. J. McGill, Introduction to Modern Information Retrieval. McGraw-Hill, Inc., 1986, p. 400.
[6] J. Rocchio, "Relevance feedback in information retrieval," The Smart retrieval system-experiments in automatic document processing, pp. 313-323, 1971.
[7] Y.-S. Lin, "The application of the term information residing in relevance feedback for query expansion," M.B.A. thesis, National Central University, Taiwan, 2015.
[8] Z.-L. Sun, "The application of semantic analysis in relevance feedback for query expansion," M.B.A. thesis, National Central University, Taiwan, 2018.
[9] W. Hersh, A. M. Cohen, L. Ruslen, and P. Roberts, TREC 2007 genomics track overview. 2007.
[10] G. W. Furnas, T. K. Landauer, L. M. Gomez, and S. T. Dumais, "The vocabulary problem in human-system communication," Commun. ACM, vol. 30, no. 11, pp. 964-971, 1987.
[11] T. Lau and E. Horvitz, "Patterns of Search: Analyzing and Modeling Web Query Refinement," in UM99 User Modeling, Vienna, 1999, pp. 119-128: Springer Vienna.
[12] S. Zhang, B. He, and W. Fan, CBIA VT at TREC 2015 Clinical Decision Support Track -Exploring Relevance Feedback and Query Expansion in Biomedical Information Retrieval. 2015.
[13] C. Silverstein, H. Marais, M. Henzinger, and M. Moricz, "Analysis of a very large web search engine query log," SIGIR Forum, vol. 33, no. 1, pp. 6-12, 1999.
[14] C. Carpineto and G. Romano, "A Survey of Automatic Query Expansion in Information Retrieval," ACM Comput. Surv., vol. 44, no. 1, pp. 1-50, 2012.
[15] F. João Pinto and C. Pérez-Sanjulián, Automatic query expansion and word sense disambiguation with long and short queries using WordNet under vector model. 2008.
[16] T.-P. Approach For, Z. Shi, B. Gu, F. Popowich, and A. Sarkar, Synonym-based Query Expansion and Boosting-based Re-ranking. 2005.
[17] L. Araujo and J. R. Pérez-Agüera, "Improving Query Expansion with Stemming Terms: A New Genetic Algorithm Approach," in Evolutionary Computation in Combinatorial Optimization, Berlin, Heidelberg, 2008, pp. 182-193: Springer Berlin Heidelberg.
[18] Q. Chen, M. Li, and M. Zhou, Improving Query Spelling Correction Using Web Search Results. 2007, pp. 181-189.
[19] G. Salton, The SMART Retrieval System—Experiments in Automatic Document Processing. 1971.
[20] M. Dillon and J. Desper, "The use of automatic relevance feedback in Boolean retrieval systems," Journal of Documentation, vol. 36, no. 3, pp. 197-208, 1980.
[21] S. E. Robertson, C. J. v. Rijsbergen, and M. F. Porter, "Probabilistic models of indexing and searching," presented at the Proceedings of the 3rd annual ACM conference on Research and development in information retrieval, Cambridge, England, 1981.
[22] C. Buckley and G. Salton, "Optimization of relevance feedback weights," presented at the Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval, Seattle, Washington, USA, 1995.
[23] R. Yong, T. S. Huang, M. Ortega, and S. Mehrotra, "Relevance feedback: a power tool for interactive content-based image retrieval," IEEE Transactions on Circuits and Systems for Video Technology, vol. 8, no. 5, pp. 644-655, 1998.
[24] R. Yan, A. Hauptmann, and R. Jin, "Multimedia search with pseudo-relevance feedback," presented at the Proceedings of the 2nd international conference on Image and video retrieval, Urbana-Champaign, IL, USA, 2003.
[25] Y. Yang, F. Nie, D. Xu, J. Luo, Y. Zhuang, and Y. Pan, "A Multimedia Retrieval Framework Based on Semi-Supervised Ranking and Relevance Feedback," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, no. 4, pp. 723-742, 2012.
[26] G. Gay, S. Haiduc, A. Marcus, and T. Menzies, "On the use of relevance feedback in IR-based concept location," in 2009 IEEE International Conference on Software Maintenance, 2009, pp. 351-360.
[27] D. Kelly and J. Teevan, "Implicit feedback for inferring user preference: a bibliography," SIGIR Forum, vol. 37, no. 2, pp. 18-28, 2003.
[28] T. Joachims, L. Granka, B. Pan, H. Hembrooke, and G. Gay, "Accurately interpreting clickthrough data as implicit feedback," presented at the Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval, Salvador, Brazil, 2005.
[29] J. Xu and W. B. Croft, "Query expansion using local and global document analysis," presented at the Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval, Zurich, Switzerland, 1996.
[30] C. Manning, P. Raghavan, and H. Schütze, "Introduction to information retrieval," Natural Language Engineering, vol. 16, no. 1, pp. 100-103, 2010.
[31] A. Rajaraman and J. D. Ullman, Mining of massive datasets. Cambridge University Press, 2011.
[32] G. Salton, A. Wong, and C. S. Yang, "A vector space model for automatic indexing," Commun. ACM, vol. 18, no. 11, pp. 613-620, 1975.
[33] T. Mikolov, G. s. Corrado, K. Chen, and J. Dean, Efficient Estimation of Word Representations in Vector Space. 2013, pp. 1-12.
[34] A. Handler, "An empirical study of semantic similarity in WordNet and Word2Vec," 2014.
[35] K. Patel, D. Patel, M. Golakiya, P. Bhattacharyya, and N. Birari, Adapting Pre-trained Word Embeddings For Use In Medical Coding. 2017, pp. 302-306.
[36] NCBI (National Center for Biotechnology Information).PubMed. (n. d. ). Available: https://www.ncbi.nlm.nih.gov/pubmed/
[37] S. Pyysalo, F. Ginter, H. Moen, T. Salakoski, and S. Ananiadou, Distributional semantics resources for biomedical text processing. 2013.
[38] A. Kosmopoulos, I. Androutsopoulos, and G. Paliouras, "Biomedical semantic indexing using dense word vectors in bioasq," J BioMed Semant Suppl BioMedl Inf Retr, vol. 3410, pp. 959136040-1510456246, 2015.
[39] Y. Wang et al., "A comparison of word embeddings for the biomedical natural language processing," Journal of biomedical informatics, vol. 87, pp. 12-20, 2018.
[40] NCBI (National Center for Biotechnology Information). MeSH. (n. d. ). Available: https://www.ncbi.nlm.nih.gov/mesh
[41] M. Taschwer, "Text-Based Medical Case Retrieval Using MeSH Ontology," in CLEF (Working Notes), 2013.
[42] K. Potts, "Web Design and Marketing Solutions for Business Websites," New York, USA: Apress, 2007, September, pp. 287-288.

指導教授

周世傑(Shih-Chieh Chou)

審核日期

2019-7-23

推文