(zidousya no uriage ga koutyou: Sales of cars were good)". 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 expressions 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 for acquiring phrases that have a particular meaning not limited to cause information. We compared our method with our previous one originally proposed for extracting phrases concerning traffic accident causes and experimental results showed that our new method outperforms our previous one." />


Cause Information Extraction from Financial Articles Concerning Business Performance

Hiroyuki SAKAI  Shigeru MASUYAMA  

Publication
IEICE TRANSACTIONS on Information and Systems   Vol.E91-D   No.4   pp.959-968
Publication Date: 2008/04/01
Online ISSN: 1745-1361
DOI: 10.1093/ietisy/e91-d.4.959
Print ISSN: 0916-8532
Type of Manuscript: Special Section PAPER (Special Section on Knowledge-Based Software Engineering)
Category: Knowledge Engineering
Keyword: 
cause information,  causal expression,  knowledge extraction,  information extraction,  

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Summary: 
We propose a method of extracting cause information from Japanese financial articles concerning business performance. Our method acquires cause information, e.g. "(zidousya no uriage ga koutyou: Sales of cars were good)". 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 expressions 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 for acquiring phrases that have a particular meaning not limited to cause information. We compared our method with our previous one originally proposed for extracting phrases concerning traffic accident causes and experimental results showed that our new method outperforms our previous one.