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Open AccessFeature PaperArticle

Estimation of Cross-Lingual News Similarities Using Text-Mining Methods

1
Izumi lab, Department of System Innovation, Graduate School of Engineering, The University of Tokyo, Hongo 7-3-1, Bunkyo-ku, Tokyo 113-0033, Japan
2
Yahoo! Japan Research, Kioicho 1-3, Chiyoda-ku, Tokyo 102-8282, Japan
*
Authors to whom correspondence should be addressed.
J. Risk Financial Manag. 2018, 11(1), 8; https://doi.org/10.3390/jrfm11010008
Received: 31 December 2017 / Revised: 23 January 2018 / Accepted: 25 January 2018 / Published: 31 January 2018
(This article belongs to the Special Issue Empirical Finance)
In this research, two estimation algorithms for extracting cross-lingual news pairs based on machine learning from financial news articles have been proposed. Every second, innumerable text data, including all kinds news, reports, messages, reviews, comments, and tweets are generated on the Internet, and these are written not only in English but also in other languages such as Chinese, Japanese, French, etc. By taking advantage of multi-lingual text resources provided by Thomson Reuters News, we developed two estimation algorithms for extracting cross-lingual news pairs from multilingual text resources. In our first method, we propose a novel structure that uses the word information and the machine learning method effectively in this task. Simultaneously, we developed a bidirectional Long Short-Term Memory (LSTM) based method to calculate cross-lingual semantic text similarity for long text and short text, respectively. Thus, when an important news article is published, users can read similar news articles that are written in their native language using our method. View Full-Text
Keywords: text similarity; text mining; machine learning; SVM; neural network; LSTM text similarity; text mining; machine learning; SVM; neural network; LSTM
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Wang, Z.; Liu, E.; Sakaji, H.; Ito, T.; Izumi, K.; Tsubouchi, K.; Yamashita, T. Estimation of Cross-Lingual News Similarities Using Text-Mining Methods. J. Risk Financial Manag. 2018, 11, 8.

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