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

  • Wang Z
  • Liu E
  • Sakaji H
  • et al.
N/ACitations
Citations of this article
11Readers
Mendeley users who have this article in their library.

Abstract

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 multilingual 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.

Cite

CITATION STYLE

APA

Wang, Z., Liu, E., Sakaji, H., Ito, T., Izumi, K., Tsubouchi, K., & Yamashita, T. (2018). Estimation of Cross-Lingual News Similarities Using Text-Mining Methods. Journal of Risk and Financial Management, 11(1), 8. https://doi.org/10.3390/jrfm11010008

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free