Extraction of Cause Information from Newspaper Articles Concerning Business Performance

  • Hiroyuki Sakai
  • Shigeru Masuyama
Part of the IFIP The International Federation for Information Processing book series (IFIPAICT, volume 247)


We propose a method of extracting cause information from Japanese newspaper articles concerning business performance. Cause information is useful for investors in selecting companies to invest. Our method extracts cause information as a form of causal expression by using statistical information and initial clue phrases automatically. Our method can extract causal expressions without predetermined patterns or complex rules given by hand, and is expected to be applied to other tasks or language for acquiring phrases that have a particular meaning not limited to cause information. We compared our method with our previous method originally proposed for extracting phrases concerning traffic accident causes and experimental results showed that our new method outperforms our previous one.


Support Vector Machine Machine Translation Business Performance Causal Knowledge Newspaper Corpus 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© International Federation for Information Processing 2007

Authors and Affiliations

  • Hiroyuki Sakai
    • 1
  • Shigeru Masuyama
    • 1
  1. 1.Department of Knowledge-based Information EngineeringToyohashi University of TechnologyToyohashi-shi, AichiJapan

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