Hiroyuki Sakai

<p>In this research, we propose a method for extracting sentences containing causal information from articles describing the market conditions of the Nikkei Stock Average. The sentences containing causal information are needed to generate market analysis comments. Our method extracts articles describing the market conditions of the Nikkei Stock Average from economic newspaper articles and extracting sentences containing causal information from the extracted articles by deep learning. Here, our method automatically generates the training data necessary to extract the articles describing the market conditions and sentences containing causal information by deep learning and achieved high accuracy. Moreover, our method extracts complementary information of the content described in the causal sentences by using economic causal-chain search.</p>

In this research, we propose a method of extracting business segments from securities reports and extracting sentences containing causal and result information concerning business performance for each extracted business segments. For example, our method extracts “In the aluminum rolled products business, shipments of high purity foils for aluminum electrolytic capacitors for industrial equipment and automotive use increased and sales increased.” as a sentence containing causal information concerning business performance. Moreover, our method estimates that the sentence belongs to business segment “aluminum”. Our method extracts “As a result of this segment sales in this segment were 105,439 million yen, operating profit was 6,697 million yen.” as a sentences containing performance result information belong to “aluminum” segment. We evaluated our method and the method of extracting sentences containing causal information attained 69.3% precision and 72.5% recall, and the method of extracting sentences containing performance result information attained 78.8% precision and 91.1% recall.