著者

酒井 浩之 西沢 裕子 松並 祥吾 坂地 泰紀

出版者

一般社団法人 人工知能学会

雑誌

人工知能学会論文誌 (ISSN:13460714)

巻号頁・発行日

vol.30, no.1, pp.172-182, 2015-01-06 (Released:2015-01-06)

参考文献数

13

被引用文献数

4

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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.