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    政大機構典藏 > 商學院 > 會計學系 > 期刊論文 >  Item 140.119/61589
    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/61589


    Title: An Intelligent information segmentation approach to extract financial data for business valuation
    Authors: 諶家蘭
    Seng, Jia-Lang;Lai, J.T.
    Contributors: 會計系
    Keywords: Intelligent information extraction;Information retrieval;Word segmentation;Knowledge base;Text processing;Natural language processing;Business valuation;Financial data;Non-financial data
    Date: 2010.09
    Issue Date: 2013-11-11 17:04:18 (UTC+8)
    Abstract: Due to an increase in the wealth of electronic resources on the Internet in the past several years, the birth of the search engine has brought the utmost convenience and efficiency for users. However, searching for data by keyword retrieval techniques in information retrieval is not contented with some users’ specific needs due to a large number of network resources and users on the Internet. Information extraction is an improvement method which extracts the important specific event or produces specific relations among information from documents. Information extraction can not only filter unnecessary information in any documents but also produce specific important messages and summaries that users are interested in. Business valuation is collecting, analysis, and applying to financial or non-financial integral information to appraise the business value. The evaluated results are used in the commerce pricing for the business decision and intangible assets. There are specific information and events about business valuation stored in the Intelligent financial statements, notes to financial statements, and financial news of Taiwan’s companies at present and data is presented by the HTML and PDF files. Hence, we developed an information extraction system of Chinese financial data for business valuation from the domestic business financial statements, notes to financial statements, and financial news as the data sources. We extracted the correct financial data and their corresponding Business Valuation Model to achieve an automatic extraction in the financial data from these different heterogeneous data sources. Users can collect the relevant valid valuation information and learn valuation models concepts within a very short time to improve accuracy and efficiency in text processing quality.
    Relation: Expert Systems with Applications, 37(9) ,6515-6530
    Data Type: article
    DOI 連結: http://dx.doi.org/10.1016/j.eswa.2010.02.134
    DOI: 10.1016/j.eswa.2010.02.134
    Appears in Collections:[會計學系] 期刊論文

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