文章基本信息

标题：Extraction of causal information from PDF files of the summary of financial statements of companies
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作者：Hiroyuki Sakai ; Hiroko Nishizawa ; Shogo Matsunami 等
期刊名称：人工知能学会論文誌
印刷版ISSN：1346-0714
电子版ISSN：1346-8030
出版年度：2015
卷号：30
期号：1
页码：172-182
DOI：10.1527/tjsai.30.172
出版社：The Japanese Society for Artificial Intelligence
摘要：In this paper, we propose a method of extracting causal information from PDF files of the summary of financial statements of companies, e.g., ''The sales of smart phones was expanded continually''. Cause information is useful for investors in selecting companies to invest. We downloaded 106,885 PDF files of the summary of financial statements of companies from Web pages of the companies automatically. Our method extracts causal information from the PDF files by using clue expressions (e.g., ''was expanded'') and keywords relevant to a company. The clue expressions are extracted from the PDF files of the summary of financial statements of companies and articles concerning business performance of companies automatically. We developed the search system which is able to retrieve causal informations extracted by our method. The search system shows causal information containing a keyword inputted by users, and the summary of financial statements containing the retrieved causal information. We evaluated our method and it attained 83.91% precision and 55.04% recall, respectively. Moreover, we compared our method with Sakai et al's method originally proposed for extracting causal information from financial articles concerning business performance of companies and experimental results showed that our method outperforms Sakai et al's method.
关键词：summary of financial statements of company ; causal information ; information extraction ; text mining