Extraction of cause information from newspaper articles concerning business performance

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.