請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/42996完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 陳建錦(Chien-Chin Chen) | |
| dc.contributor.author | Cheng-Yen Chen | en |
| dc.contributor.author | 陳政彥 | zh_TW |
| dc.date.accessioned | 2021-06-15T01:32:03Z | - |
| dc.date.available | 2009-07-23 | |
| dc.date.copyright | 2009-07-23 | |
| dc.date.issued | 2009 | |
| dc.date.submitted | 2009-07-20 | |
| dc.identifier.citation | [1] J. Allan, J. Carbonell, G. Doddington, J. Yamron and Y. Yang, 'Topic Detection and Tracking Pilot Study Final Report,' in Proceedings of DARPA Broadcast News Transcription and Understanding Workshop, 1998.
[2] J. Allan, R. Papka, and V. Lavrenko, “On-line New Event Detectioin and Tracking,” in Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval, pages 37-45, 1998. [3] J. Berg, R. Forsythe, F. Nelson, and T. Rietz, “Results from a dozen years of election futures markets research,” Technical Report, University of Iowa, 2003. [4] J. Berg, R. Forsythe, T. Rietz, “What makes markets predict well? Evidence from the Iowa Electronic Markets,” Understanding Strategic Interaction: Essays in Honor of Reinhard Selten, pages 444-463, 1996. [5] J. Berg, F. Nelson and T. Rietz, “Accuracy and Forecast Standard Error of Prediction Markets,” working paper, University of Iowa, College of Business Administration, 2001. [6] J. Berg, F. Nelson and T. Rietz, “Prediction Market Accuracy in the Long Run,” International Journal of Forecasting, Volume 24, Issue 2, pages 285-300, 2008. [7] G. Boyle, 'A Primer on Information Markets,' ISCR, Victoria University of Wellington, 2005. [8] G. Caldeira, “Expert Judgment versus Statistical Models, Explanation versus Prediction,” Perspectives on Politics, Volume 2, Issue 04, pages 777-780, 2004. [9] C. C. Chen and M. C. Chen, 'TSCAN: A Novel Method for Topic Summarization and Content Anatomy,' in Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval, pages 579-586, 2008. [10] L. F. Chien, “PAT-Tree-Based Keyword Extraction for Chinese Information Retrieval,” in Proceedings of the 20th annual international ACM SIGIR conference on Research and development in information retrieval, pages 50-58, 1997. [11] T. Dunning, “Accurate Methods for the Statistics of Surprise and Coincidence,” Computational Linguistics, Volume 19, No. 1, pages 61-74, 1993. [12] R. Forsythe, F. Nelson, G. R. Neumann, and J. Wright, 'Anatomy of an Experimental Political Stock Market,' The American Economic Review, Volume 82, No. 5, pages 1142-1161, 1992. [13] A. Gilder and K. Lerman, “Reading the Markets: Forecasting Prediction Markets By News Content Analysis,” Senior Project, CIS Dept. University of Pennsylvania, 2007. [14] G. H. Gonnet, R. A. Baeza-yates and T. Snider, “New Indices for Text: Pat Trees and Pat Arrays,” Information Retrieval Data Structures & Algorithms, Prentice Hall, pages 66-82, 1992. [15] R. Hanson, 'Decision Markets,' IEEE Intelligent Systems, Volume 14, No. 3, pages 16-19, 1999. [16] R. Kosala and H. Blockeel, “Web Mining Research: A Survey,” ACM SIGKDD, Volume 2, Issue 1, pages 1-15, 2000. [17] G. Kumaran and J. Allan, 'Using Names and Topics for New Event Detection,' in Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Proceeding (HLT/EMNLP), pages 121-128, 2005. [18] A. Leigh and J. Wolfers, “Competing Approaches to Forecasting Elections: Economic Models, Opinion Polling and Prediction Markets,” The Economic Record, Volume 82, No. 258, pages 325-340, 2006. [19] S. A. Macskassy and F. Provost, 'Intelligent Information Triage,' in Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval, pages 318-326, 2001. [20] J. M. Ponte and W. B. Croft, “A Language Modeling Approach to Information Retrieval,” Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 275-281, 1998. [21] G. Salton, J. Allan and C. Buckley, 'Automatic Structuring and Retrieval of Large Text Files,' Communications of the ACM, Volume 37, No. 2, pages 97-108, 1994. [22] G. Salton and C. Buckley, 'Term Weighting Approaches in Automatic Text Retrieval,' Information Processing and Management, Volume 24, No. 5, 1988. [23] G. Salton, E. A. Fox and H. Wu, 'Extended Boolean information retrieval,' Communications of the ACM, Volume 26, Issue 11, pages 1022-1036, 1983. [24] G. Salton, A. Wong and C. S. Yang, 'A Vector Space Model for Automatic Indexing,' Communications of the ACM, Volume 18, No. 11, pages 613-620, 1975. [25] E. Servan-Schreiber, J. Wolfers, D. M. Pennock and B. Galebach, “Prediction Markets: Does Money Matter?” Electronic Markets, Volume 14, No. 3, pages 243-251, 2004. [26] D. A. Smith, “Detecting and Browsing Events in Unstructured Text,” in Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 73-80, 2002. [27] F. Song and W. B. Croft, “A General Language Model for Information Retrieval,” in Proceedings of the 8th international conference on Information and knowledge management, pages 316-321, 1999. [28] R. Swan and J. Allan, “Automatic Generation of Overview Timelines,” in Proceedings of 23rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 49-56, 2000. [29] G. Tziralis and I. Tatsiopoulos, “Prediction Markets: An Extended Literature Review,” The Journal of Prediction Markets, pages 75-91, 2007. [30] J. Wolfers and E. Zitzewitz, “Prediction Markets,” Journal of Economic Perspectives, Volume 18, No. 2, pages 107-126, 2004. [31] Y. Yang, J. Carbonell, R. Brown, T. Pierce, B. T. Archibald and X. Liu, “Learning Approaches for Detecting and Tracking News Events,” IEEE Intelligent Systems, Volume 14, No. 4, pages 32-43, 1999. [32] Y. Yang and J. O. Pedersen, “A comparative study on feature selection in text categorization.” in Proceedings of 14th International Conference on Machine Learning (ICML-97), Volume 14, pages 412–420, 1997. [33] Y. Yang, T. Pierce and J. Carbonell, “A Study on Retrospective and On-Line Event Detection,” In Proceedings of 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 28-36, 1998. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/42996 | - |
| dc.description.abstract | 自從1990年代開始，資訊市場 (Information Markets) 已經被證實是一套有效的公共事務集體智慧整合工具。透過市場運作與交易機制，資訊市場可以蒐集與整合群眾的集體智慧，並將集體智慧轉換為市場價格，該價格反映了群眾對一公共議題的看好度與可行性，進而輔助議題決策者制定合理的公眾政策。對於每一個預測市場議題，我們可依其價格與時間的變化關係畫出一價格走勢圖，在本論文中，我們將提出一套自動化的方法來分析資訊市場價格走勢圖，藉由分析資訊市場價格波動與相關的新聞文件，我們可以找出影響議題看好度的重要新聞事件，這些重要的新聞事件將可以協助群眾及社會學者了解影響公共議題的重要因子且分析社會現象。
在我們的方法裡，我們會先找出一資訊市場價格走勢圖內顯著且重要的價格波動期間，接著我們分析該期間內與議題相關的新聞文件，我們會使用事件偵測與追蹤(topic detection and tracking)的相關技術來將新聞文件集結成許多內容相似的文件群，而每一文件群代表一新聞事件。最後，我們會使用一些統計上常用的特徵篩選法(feature selection)來挑選該期間最獨特的新聞事件來代表價格波動的影響因子。實驗結果證明，我們所提出的方法能有效的找出影響資訊市場價格波動的重要事件。此外，我們也實作出一套web-based的資訊市場分析系統，透過視覺化的操作介面，使用者可快速的瞭解一公共議題曾出現了哪些具影響性的新聞事件。 | zh_TW |
| dc.description.abstract | Since the 1990s, information markets have proved effective in predicting the outcome of public issues. A public issue is represented by an information market and all its possible outcomes form the contracts of the market. Through the mechanisms of market trading and rewarding, information markets are able to collect public’s opinions and convert the opinions into prices. The price of a contract indicates the feasibility of an outcome from public perspectives. High prices indicate that the public recognizes the outcome. By contrast, low prices mean that the outcome may not be feasible from the public’s viewpoint. The prices thus can help governments and policy makers establish reasonable policies to public issues. For each contract, we collect its price in a daily basis and propose a method to analyze the price sequence. We associate fluctuations in the price sequence with news documents and identify news events that cause the fluctuations. The identified events can help the public or social science scholars comprehend influential factors of public issues and social phenomena.
In the proposed approach, we first identify time periods accompanying significant price fluctuations. Next, we apply techniques of topic detection and tracking to cluster news documents in the periods. A cluster groups content-similar news documents and represents a news event. Finally, statistical feature selection methods are employed to identify events highly associated with the periods. The events then represent the cause of the price fluctuations and are influential to the corresponding public issue. Experiments based on a real world dataset demonstrate that the proposed method can identify influential events of information markets effectively. We also develop a prototype system based on the proposed method. The system graphically labels influential events of information markets that help users comprehend the development of public issues easily. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-15T01:32:03Z (GMT). No. of bitstreams: 1 ntu-98-R96725036-1.pdf: 1450800 bytes, checksum: 7e96401b9dacbe82cd5bec74d983d3a4 (MD5) Previous issue date: 2009 | en |
| dc.description.tableofcontents | Chapter 1 Introduction 1
1.1 Background 1 1.2 Motivation 2 Chapter 2 Related Work 5 2.1 Information Market 5 2.1.1 What Is Information Market? 5 2.1.2 The Operations in Information Market 5 2.1.3 The Existing Instance of Information Market 7 2.1.4 Period Extraction of the Price Movement Curve 8 2.2 History of Topic Detection and Tracking Study 8 2.2.1 Event Detection (Topic Detection) 10 2.3 Other Related Literature 11 Chapter 3 Methodology 12 3.1 System Architecture 12 3.2 Pat-Tree Based Chinese Keyword Extraction 13 3.3 Fluctuation Period Extraction 15 3.3.1 Definitions of IM Market and Contract 15 3.3.2 Slope 16 3.3.3 The Status of Slopes 17 3.4 Incremental Event Detection 20 3.5 Influential Event Identification 21 3.5.1 Chi-Square Testing 24 3.5.2 Log-Likelihood Ratio 25 3.5.3 Main Theme Factor 26 3.5.4 Temporal Factor 27 Chapter 4 Experiment 28 4.1 Experiment Data 28 4.2 Procedures of Experiment 30 4.3 Result 34 4.4 Independency with Main Stream 37 4.5 Analysis 39 4.5.1 Analysis of the Feature Selection Methods 39 4.5.2 Analysis of the Temporal Factor 41 4.5.3 Analysis of the Main Stream 48 4.5.4 Comparison to Baseline 49 Chapter 5 Web-based Influential Event Recommendation System 50 Chapter 6 Conclusion 54 Reference 56 | |
| dc.language.iso | en | |
| dc.subject | 資訊市場 | zh_TW |
| dc.subject | 統計式特徵擷取法 | zh_TW |
| dc.subject | 漸進式分群 | zh_TW |
| dc.subject | 價格波動 | zh_TW |
| dc.subject | 重要事件偵測 | zh_TW |
| dc.subject | event detection | en |
| dc.subject | statistical feature selection | en |
| dc.subject | incremental clustering | en |
| dc.subject | information markets | en |
| dc.subject | price fluctuation | en |
| dc.title | 應用特徵擷取法找尋資訊市場中具影響性的事件 | zh_TW |
| dc.title | Influential Event Analysis of Information Markets Using Statistical Feature Selections | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 97-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 蔡益坤(Yih-Kuen Tsay),陳孟彰(Meng-Chang Chen) | |
| dc.subject.keyword | 資訊市場,重要事件偵測,價格波動,漸進式分群,統計式特徵擷取法, | zh_TW |
| dc.subject.keyword | information markets,event detection,price fluctuation,incremental clustering,statistical feature selection, | en |
| dc.relation.page | 59 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2009-07-20 | |
| dc.contributor.author-college | 管理學院 | zh_TW |
| dc.contributor.author-dept | 資訊管理學研究所 | zh_TW |
| 顯示於系所單位： | 資訊管理學系 | |
文件中的檔案：
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| ntu-98-1.pdf 未授權公開取用 | 1.42 MB | Adobe PDF |
系統中的文件，除了特別指名其著作權條款之外，均受到著作權保護，並且保留所有的權利。