臺灣博碩士論文加值系統

隨著Web 2.0 的進步，各式各樣的網路服務使得使用者從傳統的訊息接收者，轉變為有影響力的訊息傳播者，甚至是訊息來源。因此，造就了社群網站的興起與蓬勃，社群網站是指提供「社群網路服務（Social Networking Service, SNS）」的網站，主要作用是為一群擁有相同興趣與活動的人建立線上社群。這類服務往往是基於網際網路，為用戶提供各種聯繫、交流的交互通路，如電子郵件、即時消息服務等。多數社群網路會提供多種讓使用者互動起來的方式，並為信息的交流與分享提供了新的途徑。如YouTube與Flicker分別提供了多媒體影音與照片分享的服務。
此外，社群標籤為線上社群網路不可或缺的組成成分，為使用者對於資源的描述，且可作為使用者的主觀認知與目標信息間的連結。故標籤可反映使用者特徵與行為以及資源的特性，標籤間的語義關聯性亦呈現了資源間的關係，以及反映使用者對於詞彙的使用習慣，且相較於透過搜尋引擎執行關鍵字萃取，社群網路內更包含了大量豐富的語義資訊。
故本研究基於社群標籤特性，以建構統計式同義辭典，並應用於文件分群技術，以驗證與評估此同義辭典效力。其考慮使用者在某一情境下的分群偏好，並利用社群書籤網站作為資訊來源，萃取相關書籤的標題、標籤及書籤收藏人數，以建構統計式同義辭典，並應用於個人化文件分群技術，並且與現存之情境式文件分群技術比較其分群效力，以進一步驗證與評估本研究所建構之同義辭典效力。根據本研究的實證評估結果，本研究所提出之個人化文件分群技術其分群效力遠勝於內容為基礎之文件分群技術，而略勝於以網際網路(i.e., Google Search)作為資訊來源建構之同義辭典的情境感知式文件分群技術。

The success of the Web 2.0 has made social media (e.g., blogs, forums, and social networking sites, etc.) an excellent platform for gathering user intelligence for supporting critical business intelligence applications. Social tagging system (or folksonomy) is a critical mechanism for collaboratively creating, organizing and managing the wisdom of crowds. Consequently, the social tagging information of some well-known social networking sites, such as YouTube, Flicker, and Delicious, should be tremendous resource for conducting semantic-based applications to improve the performance of existing approaches. The purpose of this study is to empirically evaluate the values of social tagging systems. Specifically, we employ the social tagging information of the Delicious, the global leader of social bookmarking service, to construct a statistical-based thesaurus which is then applied to support personalized document clustering. According to our empirical evaluation results, social tagging system indeed improve the quality of the statistical-based thesaurus in comparison with the one constructed on the basis of general-purpose search engine (i.e., Google) in supporting personalized document clustering.

書名頁 i
論文口試委員審定書 ii
授權書 iii
中文摘要 iv
英文摘要 vi
誌謝 vii
目錄 viii
表目錄 x
圖目錄 xi
第一章 緒論 1
1.1 研究背景 1
1.2 研究動機 5
1.3 研究目的 6
1.4 研究架構 7
第二章 文獻探討 8
第三章 研究方法 13
3.1 同義辭典建構 13
3.1.1 相關資訊擷取 14
3.1.2 相關權重評估 16
3.1.3 統計式同義辭典 22
3.2 個人化文件分群技術 24
3.2.1 特徵值萃取及挑選 24
3.2.2 Anchoring Term 擴充 25
3.2.3 文件表示 26
3.2.4 分群 26
第四章 實驗結果及評估 28
4.1 資料收集 28
4.2 評估準則 31
4.3 參數調整 32
4.4 分群效力比較與評估 39
4.5 不同同義辭典建構方法的影響 46
第五章 結論 49
參考文獻 51

1.Aliakbary, S., Abolhassani, H., Rahmani, H. and Nobakht, B.,“Web Page Classification Using Social Tags,”In Proceedings of the 2009 International Conference on Computational Science and Engineering, 2009, pp.588-593.
2.Al-Khalifa, H. S. and Davis, H. C.,“Measuring the Semantic Value of Folksonomies,”In Proceedings of the Second International IEEE Conference on Innovations in Information Technology, 2006, pp.1-5.
3.Biancalana, C. and Micarelli, A.,“Social Tagging in Query Expansion: a New Way for Personalized Web Search,” In Proceedings of the 2009 International Conference on Computational Science and Engineering, 2009, pp.1060-1065.
4.Brill, E.,“A Simple Rule-based Part of Speech Tagger,” In Proceedings of the Third Conference on Applied Natural Language Processing, 1992, pp.152-155.
5.Brill, E.,“Some Advances in Rule-based Part of Speech Tagging,”In Proceedings of the Twelfth National Conference on Artificial Intelligence (AAAI-94), 1994, pp. 722-727.
6.Cattuto, C., Benz, D., Hotho, A. and Stumme, G., “Semantic Analysis of Tag Similarity Measures in Collaborative Tagging Systems,”In Proceedings of the 3rd Workshop on Ontology Learning and Population (OLP3), 2008, pp.39-43.
7.Halpin, H., Robu, V. and Shepherd, H.,“The Complex Dynamics of Collaborative Tagging,”In Proceedings of the 16th international conference on World Wide Web, 2007, pp.211-220.
8.Hood, S.,“Delicious is 5!,” http://blog.delicious.com/blog/2008/11/delicious-is-5.html, 2008.
9.Hsu, M. H. and Chen, H. H.,“Tag Normalization and Prediction for Effective Social Media Retrieval,” In Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, 2007, pp.770-774.
10.Jin, S., Lin, H. and Su, S.,“Query Expansion based on Folksonomy Tag Co-occurrence Analysis,”In Proceedings of the 2009 IEEE International Conference on Granular Computing, 2009, pp.17-19.
11.Kuo, B. Y. L., Hentrich, T., Good, B. M. and Wilkinson, M. D.,“Tag Clouds for Summarizing Web Search Results,”In Proceedings of the 16th international conference on World Wide Web, 2007, pp.1203-1204.
12.Laura, G. M.,“Social bookmarking, Folksonomies, and Web 2.0 tools,”Searcher: The Magazine for Database Professionals (14:6), 2006, pp.26-38.
13.Marlow, C., Naaman, M., Boyd, D. and Davis, M., “Position Paper, Tagging, Taxonomy, Flickr, Article, ToRead,”In Proceedings of the 17th Conference on Hypertext and Hypermedia, 2006, pp.31-40.
14.Roussinov, D. G. and Chen, H.,“Document Clustering for Electronic Meetings: An Experimental Comparison of Two Techniques,”Decision Support Systems (27:1-2), 1999, pp.67-79.
15.Sebastiani, F.,“Machine Learning in Automated Text Categorization,”ACM Computing Surveys (34:1), 2002, pp.1-47.
16.Smith, G.,“ Folksonomy: social classification,” http://atomiq.org/archives/2004/08/folksonomy_social_classification.html, 2004.
17.Turney, P. D. and Littman, M.L.,“Measuring Praise and Criticism: Inference of Semantic Orientation from Association,”ACM Transactions on Information Systems (21:4), 2003, pp.315-346.
18.Voutilainen, A.,“Nptool: A Detector of English Noun Phrases,”In Proceedings of Workshop on Very Large Corpora, 1993, pp.48-57.
19.Wu, C. and Zhou, B.,“Semantic Relatedness in Folksonomy,”In Proceedings of the 2009 International Conference on New Trends in Information and Service Science, 2009, pp.760-765.
20.Yang, C. C. and Luk, J.,“Automatic Generation of English/Chinese Thesaurus Based on a Parallel Corpus in Laws,”Journal of the American Society for Information Science and Technology (54:7), 2003, pp.671-682.
21.Yang, C. S. and Wei, C.,“Context-aware Document-clustering Technique,”In Proceedings of the 11th Pacific Asia Conference on Information Systems (PACIS 2007), 2007.