Publications-Theses

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<br>諶家蘭
Chi, Yen Ping<br>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 18-Sep-2009 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
dc.contributor.advisor 季延平<br>諶家蘭zh_TW
dc.contributor.advisor Chi, Yen Ping<br>Seng, Jia Langen_US
dc.contributor.author (Authors) 吳思宏zh_TW
dc.contributor.author (Authors) Wu, Szu-Hungen_US
dc.creator (作者) 吳思宏zh_TW
dc.creator (作者) Wu, Szu-Hungen_US
dc.date (日期) 2007en_US
dc.date.accessioned 18-Sep-2009 20:14:10 (UTC+8)-
dc.date.available 18-Sep-2009 20:14:10 (UTC+8)-
dc.date.issued (上傳時間) 18-Sep-2009 20:14:10 (UTC+8)-
dc.identifier (Other Identifiers) G0094356038en_US
dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/36943-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 資訊管理研究所zh_TW
dc.description (描述) 94356038zh_TW
dc.description (描述) 96zh_TW
dc.description.abstract (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。
zh_TW
dc.description.abstract (摘要) 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.
en_US
dc.description.tableofcontents 第一章 緒論
1.1 研究背景 1
1.2 研究問題 2
1.3 研究目的 3
1.4 研究限制 3
1.5 研究流程 4
1.6 論文架構 4

第二章 文獻探討
2.1 企業評價 6
2.2 資訊擷取 7
2.3 中文斷詞 11
2.4 PDF文件格式 11
2.4.1 PDF資料結構 11
2.4.2 PDF實體結構 12
2.4.3 PDF的邏輯結構 13
2.5 擷取PDF表格 15
2.6 非財務資訊與年報 19
2.6.1非財務資訊 19
2.6.2年報中的非財務資訊 20

第三章 研究模型
3.1 PDF文件格式轉換 24
3.1.1 PDF轉HTML 24
3.1.2 PDF表格轉Excel 26
3.2 預處理程式 27
3.3 資訊擷取主程式 29
3.3.1 企業評價相關非財務性資料項列表 30
3.3.2 企業評價相關非財務性資料項關鍵詞 30
3.4 斷詞程式 32
3.5 Excel擷取程式 34
3.6 資料整合程式 36

第四章 雛型系統設計
4.1 雛型系統環境及系統架構 37
4.2 資料庫系統 38
4.3 資料來源 42
4.4 雛型系統設計與實作 43
4.4.1 後端程式 43
4.4.2 前端系統介面 45
4.4.3 後端程式介面 53

第五章 實驗設計與證明
5.1 實驗設計 58
5.2 測試樣本 58
5.3 實驗結果與驗證 59
5.4 與其他近似研究比較 60
5.4.1 近似研究(一) 60
5.4.2 近似研究(二) 60
5.4.3 綜合比較 61

第六章 研究討論與結語
6.1 研究發現 64
6.2 關鍵詞建立發現 65
6.3 結論 66
6.4 未來研究方向 67

參考文獻 69

附錄:2006年資本額五十大公司列表 72
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dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0094356038en_US
dc.subject (關鍵詞) 企業評價zh_TW
dc.subject (關鍵詞) 資訊擷取zh_TW
dc.subject (關鍵詞) Portable Document Format ( PDF )zh_TW
dc.subject (關鍵詞) 資訊檢索zh_TW
dc.subject (關鍵詞) 斷詞zh_TW
dc.subject (關鍵詞) Business valuationen_US
dc.subject (關鍵詞) Data extractionen_US
dc.subject (關鍵詞) Portable Document Format ( PDF )en_US
dc.subject (關鍵詞) Information Retrievalen_US
dc.subject (關鍵詞) Word Segmentationen_US
dc.title (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zh_TW
dc.title (題名) A Transformation Approach to Extract Annual Report for Non-Financial Category in Business Valuationen_US
dc.type (資料類型) thesisen
dc.relation.reference (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.zh_TW
dc.relation.reference (參考文獻) 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.zh_TW
dc.relation.reference (參考文獻) 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.zh_TW
dc.relation.reference (參考文獻) 4. Chen, Keh-Jiann, Ma, Wei-Yun (2002). “Unknown Word Extraction for Chinese Documents”, Proceedings of Coling 2002 (pp.169-175). Taipei, Taiwan.zh_TW
dc.relation.reference (參考文獻) 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.zh_TW
dc.relation.reference (參考文獻) 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.zh_TW
dc.relation.reference (參考文獻) 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.zh_TW
dc.relation.reference (參考文獻) 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.zh_TW
dc.relation.reference (參考文獻) 9. Kuechler, W., L. (2007). “Business applications of unstructured text, ” Communications of the ACM, Vol. 50(10).zh_TW
dc.relation.reference (參考文獻) 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.zh_TW
dc.relation.reference (參考文獻) 11. Hassan, T., & Baumgartner, R. (2005) “Intelligent Wrapping from PDF Documents”, Proceedings of the 1st International Workshop on Representation and Analysis of Web Space, Czechzh_TW
dc.relation.reference (參考文獻) 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).zh_TW
dc.relation.reference (參考文獻) 13. Mansour, N., Haraty, R.,A., Daher, W. & Houri, M. (2008) “An auto-indexing method for Arabic text,” Information Processing & Management, In Press.zh_TW
dc.relation.reference (參考文獻) 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.zh_TW
dc.relation.reference (參考文獻) 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.zh_TW
dc.relation.reference (參考文獻) 16. Morrison, P.,J. (2008) "Tagging and searching: Search retrieval effectiveness of folksonomies on the World Wide Web," Information Processing & Management, In Press.zh_TW
dc.relation.reference (參考文獻) 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.zh_TW
dc.relation.reference (參考文獻) 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.zh_TW
dc.relation.reference (參考文獻) 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.zh_TW
dc.relation.reference (參考文獻) 20. Smith, G.V.(1988). Corporate Valuation: a Business and Professional Guide. NY: John Wiley & Sons, 72-85.zh_TW
dc.relation.reference (參考文獻) 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.zh_TW
dc.relation.reference (參考文獻) 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.zh_TW
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