estimation of cross-lingual news similarities using text-mining methods

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ID: 209501
2018
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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 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.
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wang2018journalestimation Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Zhouhao Wang;Enda Liu;Hiroki Sakaji;Tomoki Ito;Kiyoshi Izumi;Kota Tsubouchi;Tatsuo Yamashita
Journal Resuscitation
Year 2018
DOI
10.3390/jrfm11010008
URL
http://www.mdpi.com/1911-8074/11/1/8
Keywords
Machine learning Neural network text mining svm

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