JSAI2020

Presentation information

General Session

General Session » J-13 AI application

[1D3-GS-13] AI application: Text mining and natural language

Tue. Jun 9, 2020 1:20 PM - 3:00 PM Room D (jsai2020online-4)

座長:梅原英一(東京都市大)

2:00 PM - 2:20 PM

[1D3-GS-13-03] Extraction of causal and complementary information for generating market analysis comments by automatic generation of training data

〇Hiroyuki Sakai1, Hiroki Sakaji2, Kiyoshi Izumi2, Tohgoroh Matsui3, Keitaro Irie4 (1. Seikei University, 2. The University of Tokyo, 3. Chubu University, 4. Mitsubishi UFJ Kokusai Asset Management Co., Ltd.)

Keywords:market analysis, causal extraction, operational efficiency improvement, text mining

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.

Authentication for paper PDF access

A password is required to view paper PDFs. If you are a registered participant, please log on the site from Participant Log In.
You could view the PDF with entering the PDF viewing password bellow.

Password