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- Title
Estimation of Cross-Lingual News Similarities Using Text-Mining Methods.
- Authors
Zhouhao Wang; Enda Liu; Hiroki Sakaji; Tomoki Ito; Kiyoshi Izumi; Kota Tsubouchi; Tatsuo Yamashita
- 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 multi-lingual text resources provided by Thomson Reuters News, we developed two estimation algorithms for extracting cross-lingual news pairs frommultilingual 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-TermMemory (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 ourmethod.
- Subjects
STOCKS (Finance); VALUE at risk; NEGATIVE binomial distribution; FOURIER transform infrared spectrophotometers; DATA analysis
- Publication
Journal of Risk & Financial Management, 2018, Vol 11, Issue 1, p1
- ISSN
1911-8066
- Publication type
Article
- DOI
10.3390/jrfm11010008