政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/36943
English  |  正體中文  |  简体中文  |  Post-Print筆數 : 27 |  全文筆數/總筆數 : 113822/144841 (79%)
造訪人次 : 51836121      線上人數 : 414
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
搜尋範圍 查詢小技巧:
  • 您可在西文檢索詞彙前後加上"雙引號",以獲取較精準的檢索結果
  • 若欲以作者姓名搜尋,建議至進階搜尋限定作者欄位,可獲得較完整資料
  • 進階搜尋
    政大機構典藏 > 商學院 > 資訊管理學系 > 學位論文 >  Item 140.119/36943
    請使用永久網址來引用或連結此文件: https://nccur.lib.nccu.edu.tw/handle/140.119/36943


    題名: 轉換年報資料以擷取企業評價模型之非財務性資料項
    A Transformation Approach to Extract Annual Report for Non-Financial Category in Business Valuation
    作者: 吳思宏
    Wu, Szu-Hung
    貢獻者: 季延平
    諶家蘭

    Chi, Yen Ping
    Seng, Jia Lang

    吳思宏
    Wu, Szu-Hung
    關鍵詞: 企業評價
    資訊擷取
    Portable Document Format ( PDF )
    資訊檢索
    斷詞
    Business valuation
    Data extraction
    Portable Document Format ( PDF )
    Information Retrieval
    Word Segmentation
    日期: 2007
    上傳時間: 2009-09-18 20:14:10 (UTC+8)
    摘要: 現今由於之前企業併購熱潮,使得企業到底價值多少?企業是否能夠還有前景?這些問題不僅僅是投資者所關心的問題,也同樣是會計師及企業評價者所關心的問題。又現今已邁入知識經濟時代,企業已從過去以土地、廠房、設備等固定資產來產生企業價值,轉而以服務、品牌、專利等無形資產為主要的企業價值時,企業的價值又要如何來估算。而這些問題都一再的顯示出“企業評價”的重要性。

    在進行企業評價之前,企業評價模型中之資料項的取得更是關係著最後評價結果的好壞。在企業評價資料項中,可分為財務性及非財務性。財務性資料項由於定義清楚,所以在資料的收集上較非財務性資料容易。但我們發現過往之資料收集方式並不足以應用在企業評價非財務性資料項的收集上,且現行大多採用人工處理資料的方式,不僅耗費大量時間及成本,又因人工輸入而有資料輸入錯誤之風險,使得資料的正確性大幅降低。故本研究提出一自動化擷取年報中企業評價非財務性資料項之方法,希望藉此方法達到簡化資料收集過程,提高資料的正確性。
    Because of the trend of the business combination, now, more and more people concern about “how much value does a business have?” And “does the business still have any perspectives?” This not only get investors’’ interest, but also the accountant and business valuator. Now we already get into a new economy, called knowledge-based economy. When the businesses are not just use fixed asset, such as facility, factory and land to earn money, but also earn their money by providing services, making brand, or sell patents for live, how to measure the business’s real value and what the real value for the business is. These problems all shows that the importance of “Business Valuation.”

    Before calculate the business value, the most important thing is to collect the data or data category for business valuation. There are two kinds of business valuation data item. One is financial data item; the other is non-financial data item. Because of the financial data item’s clear definition, the data collection process of financial data item is easier than non-financial data item. And the data collection in the past is not fit for today, and now most valuators use manual way to process these data. This way not only wastes the time and money, but also lowers the correctness and raises the risk of mistype during the process of data collection. In this thesis, we propose an approach to automatic extract business valuation data category from annual report by using the technology of data extraction.
    參考文獻: 1. Abdou, S. & Savoy, J. (2008) “Searching in MEDLINE: Query expansion and manual indexing evaluation, ” Information Processing & Management, Vol. 44(2), 781-789.
    2. Anjewierden, A. (2001). “AIDAS: Incremental Logical Structure Discovery in PDF Documents”, Sixth International Conference on Document Analysis and Recognition (pp. 0374-0378), Seattle, WA, USA.
    3. Chang, Chia-Hui, Kayed, M., Girgis, M., R. , Shaalan, K. F. (2006). “A Survey of Web Information Extraction Systems, ” IEEE Transactions on Knowledge and Data Engineering, pp. 1411-1428.
    4. Chen, Keh-Jiann, Ma, Wei-Yun (2002). “Unknown Word Extraction for Chinese Documents”, Proceedings of Coling 2002 (pp.169-175). Taipei, Taiwan.
    5. Chien, Lee-Feng (1995). “Fast and Quasi-Natural Language Search for Gigabytes of Chinese Texts”. Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval (pp.112-120). Seattle, Washington, United States.
    6. Chien, Lee-Feng (1997). “PAT-Tree-Based Keyword Extraction for Chinese Information Retrieval”, Proceedings of the 20th annual international ACM SIGIR conference on Research and development in information retrieva l (pp. 50-58). Philadelphia, Pennsylvania, United States.
    7. Embley, D. W., Fuhr, N., Klas, C. P. & Roelleke, T. (1999) “Ontology Suitability for Uncertain Extraction of Information from Multi-Record Web Documents,” ADI’99 Proceedings.
    8. Esposito, F., Ferilli, S., Basile, T. M.A., Mauro,N. D. (2005). “Intelligent Document Processing”, Eighth International Conference on Document Analysis and Recognition (pp. 1100-1104), Seoul, Korea.
    9. Kuechler, W., L. (2007). “Business applications of unstructured text, ” Communications of the ACM, Vol. 50(10).
    10. Kwok, Thomas, & Nguyen, Thao.(2006). “An Automatic Method to Extract Data from an Electronic Contract Composed of a Number of Documents in PDF Format”, The 3rd IEEE International Conference on Enterprise Computing, E-Commerce, and E-Services(pp. 33-37), Monte Carlo Resort, Las Vegas, Nevada, USA.
    11. Hassan, T., & Baumgartner, R. (2005) “Intelligent Wrapping from PDF Documents”, Proceedings of the 1st International Workshop on Representation and Analysis of Web Space, Czech
    12. Ma, Wei-Yun, & Chen, Keh-Jiann (2003) “A Bottom-up Merging Algorithm for Chinese Unknown Word Extraction”, Proceedings of ACL, Second SIGHAN Workshop on Chinese Language Processing (pp. 31-38).
    13. Mansour, N., Haraty, R.,A., Daher, W. & Houri, M. (2008) “An auto-indexing method for Arabic text,” Information Processing & Management, In Press.
    14. Meng, I-Heng. (2002). Design and Study of Semantic Discovery Methods for Extracting Knowledge from Free Text Information. Unpublished master’s thesis, National Chiao-Tung University. Hsinchu, Taiwan.
    15. Melnik S., Raghavan, S., Yang, B., & Hector, Garcia-Molina. (2001). “Building a Distributed Full-Text Index for the Web”. Proceedings of the 10th international conference on World Wide Web (pp. 396 – 406), Hong Kong.
    16. Morrison, P.,J. (2008) "Tagging and searching: Search retrieval effectiveness of folksonomies on the World Wide Web," Information Processing & Management, In Press.
    17. Pen, Chih-Jen. (2001). LIEF: An Algorithm for Learning Information Extraction Rules from Unstructured Documents. Unpublished master’s thesis, National Sun Yat-sen University, Kaohsiung, Taiwan.
    18. Ramel, J.-Y., Crucianu, M., Vincent, N., & Faure, C. (2003). Detection, Extraction and Representation of Tables, Seventh International Conference on Document Analysis and Recognition (ICDAR`03), 1, pp. 374-378, Edinburgh, Scotland.
    19. Riloff, E., & Lehnert, W. (1994) “Information Extraction as A Basis for High-precision Text Classification,” ACM Transactions on Information Systems, 12(3), 296-333.
    20. Smith, G.V.(1988). Corporate Valuation: a Business and Professional Guide. NY: John Wiley & Sons, 72-85.
    21. Song Y., & Zhang W. (2005). “Research on PDF Documents Information Extraction System Based on XML”, New Technology of Library and Information Service, 9, 10-13, China.
    22. Sweeney, S., Crestani, F. and Losada, D., E. (2008) “ ‘Show me more’: Incremental length summarisation using novelty detection, ” Information Processing & Management, Vol. 44(2), 663-686.
    23. Tsai, Yu-Fang, & Chen, Keh-Jiann (2003) “Reliable and Cost-Effective Pos-Tagging”, Proceedings of ROCLING XV (pp161-174).
    24. Tsai, Yu-Fang, & Chen, Keh-Jiann (2003) “Context-rule Model for POS Tagging”, Proceedings of PACLIC 17 (pp146-151).
    25. Tseng, Yi-Feng. (2005). The Mining and Extraction of Primary Informative Blocks and Data Objects from Systematic Web Pages, Unpublished master’s thesis, National Cheng-Kung University, Tainan, Taiwan.
    26. Vechtomova, O., & Karamuftuoglu, M. (2008) “Lexical cohesion and term proximity in document ranking,” Information Processing & Management, In Press.
    27. Wu, Chung-Hsien, Yeh, Jui-Feng, Lai, Yu-Sheng (2006) “Semantic Segment Extraction and Matching for Internet FAQ Retrieval, ” IEEE Transactions on Knowledge and Data Engineering, pp. 930-940.
    28. Zhai, Y. & Liu, B. (2006) “Structured Data Extraction from the Web Based on Partial Tree Alignment, ” IEEE Transactions on Knowledge and Data Engineering, pp. 1614-1628.
    29. 王文廷(2005)。Free-DOM:萃取鬆散文件中的重要資訊並結構化之方法。台灣大學資訊工程學研究所碩士論文。未出版,台北市。
    30. 池千駒(1998)。運用財務性,非財務性資訊建立我國上巿公司財務預警模式。成功大學會計學系碩士論文,未出版,台南市。
    31. 吳啟銘(2001)。企業評價個案實證分析(初版)。台北市:智勝文化出版社。
    32. 吳俊儀(2005)。網頁資訊擷取系統應用於電腦零組件名詞擷取之研究。國防管理學院資訊研究所碩士論文。未出版,台北市。
    33. 林千翔(2005)。基於特製隱藏式馬可夫模型之中文斷詞研究。中央大學資訊工程研究所碩士論文。未出版,中壢市。
    34. 張漢傑(2007)。破解財務危機。台北市:梅林文化事業有限公司。
    35. 陳怡雯(2003)。企業財務危機預警模式-非財務指標之運用。真理大學財經研究所碩士論文。未出版,台北縣。
    36. 黃燕萍(1999)。中文社會新聞文件資訊擷取。雲林科技大學資訊管理研究所碩士論文。未出版,雲林。
    37. 董振東、董強(2001)。面向信息處理的詞匯語義研究中的若干問題,語言文字應用,第三期,pp.27-32。
    38. 維基百科。檢索GOOGLE。線上檢索日期:2006年11月30日。網址:http://zh.wikipedia.org/wiki/Google
    39. GOOGLE。為什麼使用GOOGLE。線上檢索日期:2006年11月30日。網址:http://www.google.com/intl/zh-TW/why_use.html
    描述: 碩士
    國立政治大學
    資訊管理研究所
    94356038
    96
    資料來源: http://thesis.lib.nccu.edu.tw/record/#G0094356038
    資料類型: thesis
    顯示於類別:[資訊管理學系] 學位論文

    文件中的檔案:

    檔案 描述 大小格式瀏覽次數
    603801.pdf49KbAdobe PDF2716檢視/開啟
    603802.pdf84KbAdobe PDF2748檢視/開啟
    603803.pdf90KbAdobe PDF2833檢視/開啟
    603804.pdf226KbAdobe PDF2905檢視/開啟
    603805.pdf287KbAdobe PDF21506檢視/開啟
    603806.pdf371KbAdobe PDF22917檢視/開啟
    603807.pdf1297KbAdobe PDF23363檢視/開啟
    603808.pdf277KbAdobe PDF21763檢視/開啟
    603809.pdf222KbAdobe PDF2921檢視/開啟
    603810.pdf82KbAdobe PDF2880檢視/開啟
    603811.pdf59KbAdobe PDF21927檢視/開啟


    在政大典藏中所有的資料項目都受到原著作權保護.


    社群 sharing

    著作權政策宣告 Copyright Announcement
    1.本網站之數位內容為國立政治大學所收錄之機構典藏,無償提供學術研究與公眾教育等公益性使用,惟仍請適度,合理使用本網站之內容,以尊重著作權人之權益。商業上之利用,則請先取得著作權人之授權。
    The digital content of this website is part of National Chengchi University Institutional Repository. It provides free access to academic research and public education for non-commercial use. Please utilize it in a proper and reasonable manner and respect the rights of copyright owners. For commercial use, please obtain authorization from the copyright owner in advance.

    2.本網站之製作,已盡力防止侵害著作權人之權益,如仍發現本網站之數位內容有侵害著作權人權益情事者,請權利人通知本網站維護人員(nccur@nccu.edu.tw),維護人員將立即採取移除該數位著作等補救措施。
    NCCU Institutional Repository is made to protect the interests of copyright owners. If you believe that any material on the website infringes copyright, please contact our staff(nccur@nccu.edu.tw). We will remove the work from the repository and investigate your claim.
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - 回饋