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Chinese and Korean Cross-Lingual Issue News Detection based on Translation Knowledge of Wikipedia

  • Shengnan Zhao
  • Bayar Tsolmon
  • Kyung-Soon Lee
  • Young-Seok LeeEmail author
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 285)

Abstract

Cross-lingual issue news and analyzing the news content is an important and challenging task. The core of the cross-lingual research is the process of translation. In this paper, we focus on extracting cross-lingual issue news from the Twitter data of Chinese and Korean. We propose translation knowledge method for Wikipedia concepts as well as the Chinese and Korean cross-lingual inter-Wikipedia link relations. The relevance relations are extracted from the category and the page title of Wikipedia. The evaluation achieved a performance of 83 % in average precision in the top 10 extracted issue news. The result indicates that our method is an effective for cross-lingual issue news detection.

Keywords

Issue news detection Cross-lingual link discovery Wikipedia knowledge 

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Notes

Acknowledgments

This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (2012R1A1A2044811).

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Copyright information

© Springer Science+Business Media Singapore 2014

Authors and Affiliations

  • Shengnan Zhao
    • 1
  • Bayar Tsolmon
    • 1
  • Kyung-Soon Lee
    • 1
  • Young-Seok Lee
    • 1
    Email author
  1. 1.Division of Computer Science and Engineering, CAIITChonbuk National UniversityDeokjin-gu, Jeonju-siRepublic of Korea

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